Understanding acoustic emission testing 2006 reading 10a

My Pre-Exam Reading on Acoustic Emission Testing

Literature Reading 10 2016-10: For my ASNT Level III Examination on coming 2016 August.

26th July 2016

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Acoustic Emission Testing

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Acoustic Emission Testing

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Acoustic Emission Testing

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Acoustic Emission Testing

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Fion Zhang at Copenhagen Harbor 26st July 2016

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SME- Subject Matter Expert http://cn.bing.com/videos/search?q=Walter+Lewin&FORM=HDRSC3 https://www.youtube.com/channel/UCiEHVhv0SBMpP75JbzJShqw

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http://www.yumpu.com/zh/browse/user/charliechong http://issuu.com/charlieccchong http://independent.academia.edu/CharlieChong1

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http://greekhouseoffonts.com/

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The Magical Book of Tank Inspection ICP

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ASNT Certification Guide NDT Level III / PdM Level III AE - Acoustic Emission Testing Length: 4 hours Questions: 135 1 Principles and Theory • Characteristics of acoustic emission testing • Materials and deformation • Sources of acoustic emission • Wave propagation • Attenuation • Kaiser and Felicity effects, and Felicity ratio • Terminology (refer to acoustic emission glossary, ASTM 1316)

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2 Equipment and Materials • Transducing processes • Sensors • Sensor attachments • Sensor utilization • Simulated acoustic emission sources • Cables

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• Signal conditioning • Signal detection • Signal processing • Source location • Advanced signal processing • Acoustic emission test systems • Accessory materials • Factors affecting test equipment selection

3 Techniques • Equipment calibration and set up for test • Establishing loading procedures • Precautions against noise • Special test procedures • Data displays

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4 Interpretation and Evaluation • Data interpretation • Data evaluation • Reports 5 Procedures 6 Safety and Health 7 Applications • Laboratory studies (materialcharacterization) • Structural applications

References & Catalog Numbers ď Ž NDT Handbook, Second Edition: Volume 5, Acoustic Emission Testing Catalog Number 130 ď Ž Acoustic Emission: Techniques and Applications Catalog Number 752

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数字签名者：Fion Zhang DN：cn=Fion Zhang, o=Technical, ou=Academic, email=fion_zhang @qq.com, c=CN 日期：2016.07.29 10:35:30 +08'00' Charlie Chong/ Fion Zhang

闭门练功

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Chapter2 Fundamental Principles of Acoustic Emission Testing P. Kalyanasundaram, C.K. Mukhopadhyay, S.V Subba Rao Series Editor: Baldev Raj B. Venkatraman

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2.1 Introduction Fundamental Principles of Acoustic Emission Testing It is well known that when a solid is subjected to stress at certain levels, discrete acoustic waves are generated, which can be detected using appropriate detectors placed in contact. The phenomenon of sound generation in materials under stress is termed as acoustic emission. This phenomenon has also been referred to as stress wave emission. The American Society for Testing of Materials (ASTM) formally defines acoustic emission as 'the class of phenomena where transient elastic waves are generated by the rapid release of energy from localized sources within a material, or the transient elastic waves so generated'. In its less conventional forms, acoustic emission can be so loud that it is audible to the unaided ear.

Familiar examples of this are the creakingofwood such as timber subjected to loads near failure or the crackling of twigs, rocks and bones before breaking.

The purpose of the present book is to discuss the basic concepts of acoustic emission principles and its application as a non destructive testing and evaluation.(NDT & E) tool. This chapter provides an overview of the physical principles, advantages and applications of AE.

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2.2 History of Acoustic Emission Acoustic emission and microseismic activity are naturally occurring phenomena which man has observed from early times. Although it is not known exactly when the first acoustic emissions were heard, fracture processes such as the snapping of twigs, the cracking of rocks, and the breaking of bones were no doubt among the earliest. But the first nonestructive application of acoustic emission can be attributed to the ancient potters. Potters observed the sounds of cracking of clay vessels cooling too quickly in the kin. Through these audible acoustic emissions, the potter knew that his creation wa11 defective and structurally failing. The oldest variety ofhard fired pottery dates back to as early as 6500 B.C. In metals it would be reasonable to assume that the first true acoustic emission heard was the cry of tin the audible emission produced by mechanical twinning of pure tin during plastic deformation. This then would have occurred only after pure tin was smelted, which would be around 2500 BC. In the 19th and 20th century, incidental observations of audible sounds emitted by metals during the course of studying metallurgical phenomena, such as twinning and martensitic phase transformation studies, are reported in the literature as early as 1916 by J. Czochralski and others.

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Since then in the period 1923-1950, a number of observations relating to AE release during mechanical testing and deformation have been reported.

However, all these were just passing observations and no detailed investigations had been attempted. The first systematic studies and the genesis oftoday's technology in acoustic emission can be considered as the outcome of the work of Josef Kaiser at the Technische Hochule Munchen in Germany. In 1950 he published his Ph. D.thesis which reported the first comprehensive investigation into the phenomena of acoustic emission. The objectives of Kaiser's research was to determine from tensile tests of conventional engineering materials such as tin, lead, duralumin, copper, brass, gray iron, steel etc what noises are generated from within the specimen, the acoustic processes involved and the relation between the stress-strain curve and the frequencies noted for the various stresses to which the specimens were subjected. He made two major discoveries. The first was the near universality of the acoustic emission phenomenon by observing emissions in all the materials including dry wood he studied and second is the irreversibility phenomenon which now bears his name. He also proposed a distinction between burst-type and continuous emission.

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Following the pioneering work of Kaiser, many others such as Bradford H. Schofield, Tatro and Harold L. Dunegan initiated research in the middle 1950s and 1960s and did much to improve the instrumentation, clarify the source of acoustic emission and worked extensively in this area. In the decade of the 1960s, many engineers and scientists became interested in this method and utilized it in studies relating to materials research and characterization, nondestructive testing and structural evaluation. By the mid 1960's, Acoustic Emission Testing (AET) started to move out from the laboratory into the field environment tool primarily as an NDT tool for structural integrity evaluations of pressure vessels.

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Today AET is a matured NDT tool. Its applications are widespread and range from fundamental studies directed at clarifying the mechanisms of AE generation, correlating AE signals to physical or mechanical processes, extending the knowledge of material behavior to non-destructive testingand evaluation of industrial components and structures. With the advancement in electronics and computer technology, AE instrumentation has also improved and the applications have spread to different fields such as nuclear, aerospace, chemical plan is and process industries etc. Today, two of the most successful practical applications in which AE has proved to be a robust NDE method include periodic and continuous monitoring of pressure vessels and pressure containment systems to detect dangerous defects such as cracks and detection of incipient fatigue failures in aerospace and engineering structures.

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2.3 Principles of Acoustic Emission Testing and AE Phenomenon Acoustic Emission refers to the class of phenomenon where transient elastic waves are generated due to the rapid release of energy from localized source or sources (origin of emission) within a material. The generation of AE is a mechanical phenomenon, and can originate from a number of different mechanisms. Mechanical deformation and fracture are the primary sources of AE, but phase transformation, corrosion, friction and magnetic processes among others also give rise to AE. The energy thus released travels as a spherical wave front and can be picked up from the surface of a material using highly sensitive transducers, usually piezoelectric type placed on the surface ofthe component. Sensors are coupled to the structure by means of a fluid couplant and are secured with tape, adhesive bonds or magnetic holdowns. The output of each piezoelectric sensor (during structure loading) is amplified through a low-noise preamplifier, filtered to remove any extraneous noise and further processed by suitable electronic equipment and analysed to reveal valuable information about the source causing the energy release.

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Various types of other sensors are strain gages, accelerometers, electromagnetic acoustic transducers and optical or fibre-optic interferometers.

The frequency range of acoustic emission phenomena extends from the infrasonic(< 16Hz) into the ultrasonic range. This is shown in Fig. 2.1. The largest and therefore the longest events such as earthquakes are found at the lowest end of the scale while frequencies in the audible range occur predominantly in micro seismology i.e. during fracture phenomena in rocks.

Acoustic emission in the proper sense covers the audible frequencies and the ultrasonic range. In some cases, frequencies of 30 MHz and higher have been recorded.

The measurements of Kaiser were in the audible range while today, measurements are carried out in the ultrasonic range between 50 kHz and 1.5 MHz. At higher frequencies, the acoustic emission is not intense enough in most cases and the material also absorbs large parts of the signal. The lower frequency limit is primarily set by the background noise.

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Fig. 2.1 Frequency range of "acoustic emission" phenomena.

500Hz?

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Acoustic Emission refers to the class of phenomenon where transient elastic waves are generated due to the rapid release of energy from Localized source or sources (origin of emission) within a material. â&#x2013; It is a mechanical phenomenon, and can originate. from a number of different mechanisms.

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2.4 Sources of AE Sources of AE include many different mechanisms of deformation and fracture. The largest naturally occurring sources are the earthquakes and rock bursts while the smallest sources are the dislocations, slip, twinning, etc. The other typical sources are initiation and propagation of cracks, sudden reorientation of grain boundaries and bubble formation during boiling or martensitic phase transformation. Thus acoustic emission sources can be classified as macroscopic and microscopic sources. The term macroscopic refers to circumstances where a relatively large part of the test material is contributing to the emission phenomenon whereas in the case of microscopic sources, it is the individual events or micro-mechanisms such as dislocation motion, slip formation, microcleavage fracture etc. that act as sources of AE.

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Apart from these, there are other mechanisms such as (1) leaks and (2) cavitations, (3) friction in rotating equipments, (4) growth or realignment of magnetic domains (magnetic barkhausen noise effect) all of which release emissions. These also fall under the definition of acoustic emission. However, such sources are termed as secondary sources or pseudo sources, to distinguish them from the classical acoustic emissions arising due to mechanical deformation of materials that are subjected to stressing.

Table 2.1 below summarizes the various mechanisms which act as sources of AE. Some of the source characteristics that affect the acoustic waves are the time response of the change in internal stress, its magnitude and the area or volume in which it is active. Orientation of the source also plays a part.

However, accurate determination of source parameters is a complex task.

The same source mechanism can have different characteristics in different materials. It can also significantly differ for different loading conditions within the same material.

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Sources of AE include : earthquakes and rock bursts, dislocations, slip, twinning, â&#x20AC;˘ initiation and propagati'on of cracks, sudden reorientation of grain boundaries and bubble formation during boiling or martensitlc phase transformation. Thus acoustic emission sources can be classified as macroscopk and microscopic sources.

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2.5 AE Signals The emissions from various sources outlined above are released as acoustic energy impulses. The energy thus released travels through the structure as a spherical elastic wave to a detector, normally a piezo electric transducer which converts this acoustic impulse into an electrical signal. This electrical signal is then suitably processed and analysed to reveal information about the source causing the energy release. Two types of signals can be recognized in general acoustic emission.

These are (a) Burst Emission (b) Continuous Emission

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?

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barkhausen noise effect

Burst Emission: Burst emissions are discrete type of signals of very short duration (ranging from a few microseconds to a few milliseconds) and hence of broad frequency domain spectrum (broad frequency domain spectrum). On the screen or monitor, they appear as individual signals or single needles well separated in time. Although these signals are rarely simple needle like, they usually rise rapidly to a maximum amplitude and decay in an exponential way to the level of background noise. Fig. 2.2 shows a typical AE burst signal.

Burst signals are characteristic of crack growth and propagation and are also observed during twin formation as with the tin cry and micro-yielding.

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Fig. 2.2 Typical AE burst signal

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Continuous emission: If the acoustic impulses are emitted close to one another or if the burst rate is very high then the signals occur very close and sometimes even overlap (hits not discernible). In such cases, the emissions are termed as continuous. In this type of emission, one cannot observe the individual signals (hit? Count?) separately. A typical continuous emission is shown in Fig 2.3.

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Fig. 2.3 Typical AE continuous signal

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2.6 Factors affecting Acoustic Emission Acoustic emission is the elastic energy spontaneously released by materials undergoing deformation. AE is thus a wave phenomenon and AE testing uses the attribute or characteristics of these waves to characterize the material/process. Acoustic emission waveform is the convolution result of three effects; generation at the source, propagation and measurement. Two of the most common waveform parameters are frequency and amplitude.

As indicated earlier, AE is a wide band phenomenon and frequencies can range from audible range to about 50 MHz. The observed frequency spectrum of the AE signals greatly depends on the geometry and acoustic properties of the specimen and characteristics of the sensor. In general practical applications, the background noise governs the lower frequency limit which is normally about 10 kHz while the upper frequency limit is dictated by wave attenuation. Most of the practical applications of AE testing are carried out in the frequency range of about 100 kHz to 300 kHz. The sensitivity of AE method is primarily governed by the background noise. For the AE signal to be discernible, its amplitude should be clearly above the noise level.

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AE from metals, wood, plastic and other sources can generate signals ranging from fraction of micro-volts to more than hundred volts. The dynamic range ofthe signal amplitude from a test object may be 120 dB (V). When the signal amplitudes are very low, appropriate amplification using preamplifiers and signal conditioning would be required to visualize and interpret the AE siguals reliably. Apart from this, prior to any experimentation, the noise sources should be identified and then removed or inhibited or limited. Table 2.2 lists the factors that affect the relative amplitude of AE levels. These factors are just indicative and should not be considered as absolute.

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Acoustic emission waveform is the convolution result of three effects; generation at the source, propagation and measurement. â&#x20AC;˘ Two of the most common waveform parameters are: frequency and amplitude.

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Table 2.2 Factors Affecting Emission levels

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2.7 Characteristic of AE Technique Acoustic emission is primarily a passive NDT method (passive in term that no intentional added stimuli) that monitors the dynamic redistribution of stresses within the material or component. Hence the method is effective only when the structure or component is loaded or subjected to stresses. Simple examples ofthese loading or stresses include tensile or bend testing and pressurizing the component.

2. 7.1 Kaiser Effect Josef Kaiser observed that when materials are stressed, emissions occur and when the stress is relaxed the emissions cease and no new emissions will occur until the previous maximum stress level has been exceeded. This effect of irreversibility has been named as Kaiser Effect in honor of Kaiser who discovered it and has proved to be very useful in many of the AE applications. The degree to which this effect is present varies between metals. In some alloys and materials, the effect cannot be measured. This effect is effectively utilized in assessing structural integrity of components and diagnosing damage in pressure vessels and other engineering structures during proof testing.

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Should the vessel suffer no damage during a particular working period, the Kaiser effect dictates that no emission will be observed during the subsequent proof loading. In the event of discontinuity growth during a working period, subsequent proof loading would subject the discontinuity in the material to higher stresses than before, and the discontinuity would emit.

Emission during the proof loading is therefore a measure of damage experienced during the preceding working period. (Dunegan corollary) Because of Kaiser effect, each signal can occur only once and hence any AE inspection has to be carefully planned and signals recorded and interpreted reliably.

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When materials are stressed, emissions occur and when the stress i's relaxed the emission cease and no new emissions will occur until the previous maximum stress level has been exceeded. This effect of irreversibility is known as Kaiserâ&#x20AC;&#x2122;s Effect.

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2.7.2 Felicity Effect The concept of Kaiser effect in composite materials is slightly different from that observed in metallic materials. Specifically in fiber reinforced plastic components, emission is often observed at loads lower than the previous maximum, especially when the material is in poor condition or close to failure.

This breakdown of the Kaiser effect has been successfully used for predicting failure loads in composite pressure vessels. This appearance of significant acoustic emission at a stress level below the previous maximum applied stress level has been exceeded is defined as Felicity Effect. Thus in successive loading cycles in composite materials, the acoustic emission activity starts when the stress value is a fraction of the previous high value usually ranging from 85% to 95%. The ratio between the applied load or pressure at which the acoustic emission reappears during the next application of loading and the previous maximum load applied is termed as the felicity ratio. This ratio is always less than one. It has been observed that Kaiser effect fails most noticeably in situations where time dependent mechanisms control the deformation processes. Apart from composite materials, other cases where the Kaiser effect has been observed to fail are corrosion processes and hydrogen embrittlement.

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In fiber reinforced plastic components, emission is often observed at Loads Lower than the previous maximum, especially when the material is in poor conditjon or close to failure. This breakdown of the Kaiser Effect Is called Felicity Effect.

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2.8 Comparison of AE with other NOT .Methods In the conventional non-destructive methods, some form of energy is fed into the material (active instead of passive) , which interacts with the flaws and defects in the material and provides evidence of the same. In acoustic emission, we detect the energy that is released from the flaw or defect when the same is stressed (of course obeying the Kaiser effect). AE is thus a process in which dynamic or active flaws are detected.

This technique also provides us with the dynamic characteristics of a flaw or defect such as its growth, growth rate, critically and intensity.

Acoustic Emission inspection is a powerful aid to materials testing and the study of deformation, fracture and corrosion. It gives an immediate indication of the response and behaviour of a material under stress, intimately connected with strength, damage and failure.

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AET - This technique also provides us with the dynamic characteristics of a flaw or defect such as its:  growth,  growth rate,  critically and  intensity.

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2.9 Merits of Acoustic Emission Technique 1. AE technique gives dynamic characteristics of active defects.

2. It is a volume technique i.e. the entire structure can be covered in single inspection.

3. AE data gives real time record of progressing damage.

4. AET can be used for location of active flaws in large components.

5. It can distinguish different types of active defects i.e. source characterisation is possible. (to a certain extent? Limited characterization capability?)

6. AE is non-directional and hence a sensor located anywhere on the test object can detect emission.

7. Though initial cost is comparable with other NDT techniques, operational cost is a minimum for AET.

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AET- Volume Method

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2.10 Limitations  Usually transducer has to be placed on the structure under test. (EMAT transducer could to used to avoid contact?)  Test object has to be stimulated to make the defects active. (active or passive?)  Extraneous noise is a serious problem in AE data analysis.  Appropriate loading levels have to be determined taking into account the Kaiser effect.

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2.11 Applications Acoustic Emission Technique (AET) is a relatively recent entry into the field of non-destructive testing and evaluation. In a relatively short span, it has shown a very high potential for material characterisation and damage assessment in conventional as well as non-conventional materials. Due to its complementary nature to the conventional NDE methods, it is utlilised for a wide range of applications. One of the advantages of AE technique is the ability to monitor online the entire structure or only a part of it. The other advantage of AE which makes it an industrial NDT tool is the possibility for remote monitoring.

It requires lesser accessibility of a component i.e. just enough to place a transducer. All these features make the technique suitable for field tests and in-service inspection of structures as on-line method. Since the highly sensitive transducers can pick up minute mechanical disturbance caused by any dynamic process, this technique has the potential in tackling a variety of problems associated with different applications. The variety of possible applications is summarized in Table 2.3.

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Chapter 3 AE System, Sensors and Instrumentation P. Kalyanasundaram, C.K. Mukhopadhyay, S.V Subba Rao Series Editor: Baldev Raj B. Venkatraman

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3.1 Acoustic Emission Test Equipment The equipment for processing AE signals is available today in a variety of forms, ranging from single or dual channel systems to multi channel systems.

Depending on the need and type of applications, systems can vary from small rugged portable units to large PC based systems with a variety of software options for R & D applications. The components common to all these systems include sensors (transducers), preamplifiers, band pass or high pass filters, amplifiers and signal conditioning circuits. Auxiliary instrumentation to assist in displaying the signals, analyzing and recording vary depending on the application and user. A schematic of a simple and typical four channel system is given in Fig 3.1 below. Detailed overview of the real time monitoring systems and computerized systems are presented at the end of this chapter.

We outline below the various individual subunits of an AE system along with their characteristics and functional applications.

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Fig. 3.1 Schematic diagram of a basic four-channel acoustic emission testing system

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3.2 Sensors The heart of the AE system is the sensor. The function of the sensor is to convert the acoustic wave energy emitted by the source into usable electrical signal typically voltage time signal. This voltagetime signal is used for all subsequent steps in the AE technique. Acoustic emission sensors can be based on different physical principles. The signals can be generated by electromagnetic devices such as phonograph pickup by capacitive microphones, by magneto restrictive devices or by piezo electric devices. The requirements of an AE sensor are: 1. High sensitivity 2. Ruggedness 3. Wide bandwidth in the case of broad band sensor and narrow bandwidth in the case of resonant sensor 4. Fidelity

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Presently piezoelectric sensors are the most widely used because of the broader bandwidths that can be obtained with little or no reduction in sensitivity. Figure 3.2 is a typical schematic construction of an AE sensor.

The AE sensor normally consists of several parts. The active element is the piezoelectric element with electrodes on the top and bottom faces. One electrode is connected to the signal lead while the other electrode is connected to the electrical ground. The entire sensor is appropriately packaged in a metal case, which also acts as a shield to minimize electromagnetic pick-ups. A wear plate is also provided to protect the piezoelectric element. The AE sensor is mounted/attached to the surface of the test object using adhesive tapes, strap ons or magnetic holders.

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Sensor: Converts the acoustic wave energy emitted by the source into usable electrical signal. Couplant: Provides a good acoustic contact between the test object and the sensor.

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Fig. 3.2 AE sensor

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3.2.1 Couplants The purpose of a couplant is to provide a good acoustic contact between the test object and the sensor.

Couplants should have the following characteristics: 1. High wettability 2. Corrosion resistance 3. Sufficient viscosity 4. Easy removal Some commonly used couplants are natural wax, silicone grease, epoxy resin and propylene glycol.

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3.2.2 Waveguides In some practical applications, it may not be possible to place the sensor directly on the surface of the component for AE monitoring (e.g. in high temperature applications). In such cases, a mechanical device called a waveguide is used. The waveguide isolates the AE sensor from the adverse environmental conditions of high temperature/nuclear radiations etc while maintaining acoustic communication between the object/component under investigation and the sensor. A typical waveguide is shown in Fig. 3.3. The wave guide is made by welding typically a 6mm diameter rod of AISI type 316 stainless steel with the test piece. At the other end of the rod, a conical piece of the same material is welded. The sensor is mounted on the face of the conical piece to avoid damage due to the high temperature environment. In a similar manner, waveguides for other applications can be constructed.

In geological applications, a metal waveguide has the additional benefit of being a more efficient propagation medium than the natural material. (?)

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Waveguide length may vary for different requirements, but they all are based on welded design for maximum life. Wave guides generally perform best when welded directly to the structure being tested, but appropriate mechanical attachment may be used in circumstances where welding is not practical. While designing a waveguide, it should also be kept in mind that waveguide is a non load- earing attachment and not essential to the operation of the unit.

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Fig. 3.3 A typical wave guide

At the other end of the rod, a conical piece of the same material is welded.

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3.3 AE Instrumentation The usual instrumentation for analyzing the AE signals received from the sensor consists of preamplifiers, filters, amplifiers, signal conditioning circuits, signal processing units, storage and display units. These are described below.

3.3.1 Preamplifiers Pre-amplifier is the first stage of the instrumentation system and its main function is to enhance the signal level against noise. Since the sensor produces charge proportional to the source intensity, the pre-amplifier must be located near the sensor. Hence normally a preamplifier is used along with the transducer and the two together forms the front-end of the AE instrumentation. The preamplifiers are used:

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1. To amplify the small sensor signals so that they can be transmitted over long signal cables.

2. To match high impedance of sensors to low impedance of signal cable (typically 20K ohms to 50 ohms).

3. Low impedance cables pick up less airborne electrical interference. To provide a means of common mode rejection to reduce electrical pick-up from sensor and sensor cable.

The desirable characteristics for a good AE preamplifier are low noise, moderately high gain, low output impedance, good dynamic range, high stability, good common mode rejection and input impedance matching to the sensor. The typical specifications of the pre-amplifier are:

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Gain Bandwidth Input impedance Output voltage Dynamic range CMRR Noise

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40/60 dB 20-1200kHz I0K/15pf 20Vpp 90dB 55 dB 2 micro volts

The instrumentation for the AE system consists of preamplifiers, filters, amplifiers, signal conditioning circuits, signal processing units., storage and display units.

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3.3.2 Decibel and Gain Decibel (dB) is the log ratio. For voltage ratio (e.g. gain) dB = 20 logV2/V1 Where, V2 = output voltage Vl =input voltage Example: An amplifier that produces one volt of output for a one millivolt input has a gain of 60 dB Gain (dB)= 20 log V2/ V1 = 20 log 1000 = 20 x 3 = 60

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3.3.3 Filters Filter plays an important role in allowing the amplified signal from sensor and attenuating unwanted noise. An ideal filter of passive or active network allows the desired frequencies with unit gain and rejects unwanted frequencies.

Filter with flat frequency response for desired frequencies and sharp cut off for unwanted noise is required. Depending upon noise considerations, the operating bandwidth frequency is chosen. So, filters are designed for different bandwidths and can be plugged to preamplifiers to meet the specific requirements. Typically low pass, band pass or high pass filters can be used.

A low-pass filter is a filter that passes low frequencies well, but attenuates frequencies higher than the cutoff frequency.

A high pass filter works just the opposite allowing only the higher frequencies above the cut off.

A band pass filter allows only the band of frequencies and can be considered as a combination of low pass and high pass filters. Band pass filters with a bandwidth ranging from 100 kHz - 300 kHz are widely used during AE experimentation.

Fig. 3.4 illustrates a low pass, high pass and band pass filters.

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Fig. 3.4 Low pass (top), high pass (middle) and band pass (bottom) filter characteristics

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Fig. 3.4 Low pass (top), high pass (middle) and band pass (bottom) filter characteristics

Flat Frequency Response

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Filter plays an important role in allowing the amplified signal from sensor and attenuating unwanted noise. Filter with flat frequency response for desired frequencies and sharp cut off for unwanted noise is required. Typically low pass, band pass or high pass filters can be used.

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3.3.4 Amplifier and Measurement Circuitry The output of the filter is fed to an amplifier where the signal is further amplified. Amplifier gains in the range of 20 to 60dB are most commonly used.

After amplification, the signal is processed to reveal information about the source and its characteristics using the measurement circuitry and processing software. The level of processing to which the signal is submitted depends upon the size and cost of the system. In small portable instruments, acoustic emission events or threshold crossings may simply be counted and the count then converted to an analog voltage for plotting on a chart recorder. In more advanced hardware systems, provisions can be made for energy or amplitude measurement, spatial filtering, time gating and automatic alarms.

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3.3.5 Data Display and Recording Techniques The acoustic emission technique being sensitive to many mechanical phenomena, a huge amount of data gets generated. Sieving out the relevant data, and proper presentation of only useful information from the total data set and the interpretation is an arduors task. Choice of instrumentation for data acquisition and analysis and as output device has to be done depending upon the type of application. An almost indispensable tool for acoustic emission work is the Cathode Ray Oscilloscope (CRO).

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A few minutes of observation of the amplified signal on the oscilloscope screen is more valuable to a new experimenter than any amount of verbal description. An expert can have a feel of the type of signal and often can judge qualitatively the nature and severity of the source emitting these signals.

An X-Y recorder can be a useful output device to obtain the correlation of an AE parameter with respect to either time or any other test parameter (stimulus). The hard copies can be preserved as records for documentation.

An Root Mean Square (RMS) voltmeter with a strip chart recorder and a frequency analyzer are well¡ known instruments used for AE signal parameter measurements. Electronic counters are most widely used in AE measurement to keep track of number of events as well as ring down counts per event. Data presentation and interpretation requires skill and experience from the user, as there would be no definite absolute standards to compare with. The type of data analysis, presentation and interpretation depends on what we are actually expecting as output from the AE test, whether it is only detection of a flaw or location of a flaw or bringing out the severity of the flaw.

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A person with a few years of experience in using the technique and having knowledge about the basic phenomena occurring during tests will be in a position to identify the type of source just by observing the amplified signal on a CRT. Activity of an AE source is normally measUr-ed by event count or emission count. These are described in section 3.4.

Categorically, the sources can be divided into three types. A source is considered to be active if its counts continue to increase with increasing or constant simulation. A source is said to be critically active if the derivative of its event count, continuously increases with time at constant stimulus with time. An intensity measurement of a source is its average amplitude per event or energy per event. The activity or intensity of AE source helps in evaluating the integrity of structures or components under test. In the case of multiple AE sources, these emission counts may not yield enough information for distinguishing different sources. Distribution plots are often used to identify and classify different types of sources existing simultaneously. Distribution of amplitude or event duration is known to be key parameters for this type of classification. For example in case of composite materials, it is found that three AE sources namely matrix cracking, interface de bonding and fibre failure could be distinctly separated by peak amplitude distribution.

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In many cases a single AE parameter distribution may not be able to throw much light on the source characterization. Cross-plots taking two parameters at a time can be tried and multiparameter analysis using computers are very common these days. With the leaps in computer and software technology, pattern recognition and cluster analysis are becoming increasingly popular in AE source characterization. Frequency domain analysis using Fast fourier transform FFT is most often adhered to in case of continuous type of emission. With this type of data handling, leakage source detection and characterization are carried out with remarkable success. In applications where the main objective of the test is to locate the AE source by planar location, the data presentation will be in the form of a layout giving relative activity region-wise. In linear location, a distribution plot can present the output of the whole test at a glance effectively.

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With the type of electronics and computers we are having these days, potentiality of acoustic emission technique can be exploited in a large number of applications in various fields of engineering and industries through appropriate application of advanced signal characterisation methods.

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Choice-of instrumentation for data acquisition and analysis and as output device has to be done depending upon the type of application. Cathode Ray Oscilloscope, x-y recorder, RMS voltmeter, Electronic counters are some of these output devices. â&#x20AC;˘ Activity of an AE source fs normally measured by event count or emission count.

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Source Classification: Distribution plots are often used to identify and classify different types of sources existing simultaneously. Distribution of amplitude or event duration is known to be key parameters for this-type of classification.

Distribution plots- amplitude Distribution plots- event duration Distribution plots- energy MARSE?

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3.4 Wave form Characteristics AE signals as received by the transducer contain information on (a) the rate of emissions (average frequency?) (b) frequencies of the emitted waves (Fourier transformation?) (c) amplitudes within the emitted waves (d) Energy information about the emitted waves (RMS/ MARSE?)

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AE monitoring is usually carried out in the presence of continuous background noise. A threshold detection level is normally set above the background noise level. Reliable analysis of AE data requires that appropriate parameters be extracted from the AE signals. The characteristics of a typical acoustic emission wave are: Event, Ring down count (RDC), Peak Amplitude, Rise time, Event duration, Energy and Signal level (RMS voltage).

These are represented in Fig.3.5 and discussed below:

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The ccharacteristic of a typical acoustic Emission are: Event, Ring down count(ROC), Peak Amplitude, Rise time, Event duration, Energy, Signal Level (RMS voltage)

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Fig. 3.5 AE Characteristics

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The acoustic emission signals are modelled in the literature as a decaying sine wave, which canbe represented by the function

Y(t) = A0 exp (-Bt) Sin (Ď&#x2030;t) Where: Y(t) =output of the sensor A0 = Amplitude, B is the damping factor and Ď&#x2030; is the angular frequency.

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3.4.1 Event, Event Duration and Event Count An AE event is defined as a microstructural displacement or defect growth that produces elastic waves in the material under load or stress. The event duration is the time between the first and the last threshold crossings. This is schematically illustrated in Fig. 3.6. The duration can be a useful parameter when measuring the relative durations of the signals from the same test.

A change in either average signal duration or the distribution of durations can indicate either a change in the signal path to the sensor or a change in the generating mechanism. Both can be important in structural tests.

For example, in a glass fiber composite, matrix crazing generally produces short duration signal while propagating cracks produce long duration signals.

The event count is defined as the number of acoustic emission events and is obtained by counting each discerned acoustic emission burst as shown in Fig.3.7. (hit?)

Note: Event durations could be so short that the AE become continuous instead of burst signals.

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Fig. 3.6 Schematic illustrating Counts, Event Duration, Rise and Decay Time and amplitude of AE Signal

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Fig. 3.6 Schematic illustrating Counts, Event Duration, Rise and Decay Time and amplitude of AE Signal

amplitude

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Fig. 3.7 Event counting. The number of events here is 2.

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3.4.2 Rise time and Decay Time Rise time refers to the time required for the signal to reach its peak amplitude and is normally counted from the time between first threshold crossing and the peak amplitude while decay time refers to the time taken by the signal to decay from its peak value to just above threshold level. The rise time and decay times are illustrated in Fig. 3.6.

3.4.3 Ring Down Count The ring down count is the number of times the acoustic emission exceeds a preset threshold during any selected portion of a test. The ring down counting is also called acoustic emission count (Fig.3.8).

A single acoustic emission (hit) event can produce several counts. A larger event requires more cycles to ring down to the trigger level and will produce more counts than a smaller event. This provides a measure of the intensity of the acoustic emission event. The counts are widely used as a practical measure of acoustic emission activity. Correlations also have been established between total counts, count rate and various fracture mechanics parameters such as stress intensity factor or fatigue crack propagation rate.

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Ring Down Count Correlations also have been established between total counts, count rate and various fracture mechanics parameters such as stress intensity factor or fatigue crack propagation rate.

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Fig. 3.8 Threshold crossing counting or ring down counting

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3.4.4 Peak Amplitude The peak amplitude is the peak value of the largest excursion attained by the signal wave form from an emission event (Fig.3.9). The peak amplitude can be related to the intensity ofthe source in the material producing an acoustic emission. The peak amplitude measurements are generally performed using a log amplifier to provide accurate measurement of both large and small signals. Amplitude distributions have been correlated with deformation mechanisms in specific materials. Acoustic emission activity is attributed to the rapid release of energy in a material. Hence, the energy content of the acoustic emission signal can be related to the energy release and the true energy has been observed to be directly proportional to the area under the curve encompassing the acoustic emission waveform. Energy measurements offer advantage over other parameters such as counts or ring down counts in that energy measurements can be directly related to important events such as deformation mechanisms and strain rate.

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3.5 Limitations of Waveform Parameters and Need for Signal Analysis In general, measurement of parameters such as events, ring down counts and energy provide valuable information about the source and also its severity. This information would be useful in deciding whether the damage is accumulating and structure under question can continue in service. However, as mentioned earlier, AE signals are always accompanied by unwanted noises. For AE to be used effectively, these noise sources should be identified and eliminated if possible. While total removal of such noises is impossible, the signal to noise ratio can be increased suitably through appropriate signal analysis to enhance the reliability of interpretation. Some of the typical conventional and advanced signal analysis approaches are described below.

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Fig. 3.9 Measurement of peak amplitude

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3.6 AE Signal Anaiysis A signal can be defined as a detectable transmitted energy that can be used to carry information. It can be a time-dependent variation of a characteristic of a physical phenomenon, used to convey information.

Conventional analysis interprets the signal by its amplitude level at a particular instant. But in reality a signal contains much more information in its frequency domain from which techniques such as correlation : (1) spectral analysis, (2) cluster analysis, (3) artificial neural network, (4) pattern recognition etc. (5) wavelet transform (6) statistical analysis. can be used to extract valuable information about source, specimen, environment, etc.

AE signal analysis can be divided into traditional methods and waveform analysis methods. This is illustrated in Fig. 3.10.

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Fig. 3.10 AE signal analysis methods

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A signal contains much in its frequency domain. The techniques such.as correlatiohr spectral analysis, cluster analysis, artificial neural network pattern recognition etc. can be used to bring out informatlon about source, specimen and environment.

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3.6.1 Spectrum Analysis Acoustic emission signals have the same random character as the sources from which they are derived. If two different instruments (flute and violin) play the same musical note the human brain can easily distinguish them by signal analysis. For signal analysis, the most important features are the average repetition rate of the signals, the individual bursts and the frequency content.

The voltage-time curve for an AE signal is shown in Fig.3.11(a) and the corresponding frequency spectrum for this curve in Fig. 3.11 (b).

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The frequency spectrum of an AE signal is determined by the: ■ band-pass of the amplification system, ■ the frequency response of the sensor, ■ the geometry and AE characteristics of the sample and ■ the characteristics of the source.

The norm for AE is that no two signals have exactly the same frequency content. The variation may be small for a localized source in a well defmed geometry and the homogeneity in the material. Very large variations will occur in heterogeneous materials such as composites and in widely scattered sources in complex geometries.

Each source thus has its own typical frequency spectrum distribution and by the spectral analysis in frequency domain source characterization is possible.

The frequency range of various types of acoustic emission studies is given in Fig.3.12.

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Fig. 3.11 (a) Typical Voltage- Time curve of an AE signal

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Fig. 3.11 (b) Frequency spectrum of the signal in (a).

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Fig. 3.12 Frequency range of various types of acoustic emission studies.

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The norm for AE is tnat no two signals have exactly the same frequency content. For a well defined geometry homogeneous material, the variation is small. Large variation will occur in heterogeneous materials Like composites.

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Spectrum Analysis

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3.6.2 Cluster Analysis In nature, it is common to find the same type of flora and fauna cluster together in areas based on environmental and other conditions. Cluster analysis classifies a set of observations into two or more mutually exclusive unknown groups based on combinations of properties. The purpose of cluster analysis is to discover a system of organizing observations into groups, where objects of the same group share properties in common. It is well recognized that it is easier to predict behaviour or properties of people or objects based on groups than to deal with individuals separately since members of each group would be sharing similar properties. This concept is also applied to AE signals as well. Clustering techniques can be divisive (begins with all cases in one cluster and the cluster is gradually broken down into smaller and smaller clusters) or agglomerative techniques starting with (usually) single member clusters and gradually fused until one large cluster is formed. A variety of clustering algorithms have been developed. These include:

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1. Average linkage clustering: dissimilarity among clusters may be calculated by cluster average values. The most common is Un-weighted Pair-Groups Method Average. Centroid, or Unweighted Pair-Groups Method Centroid, uses the group centroid as the average. The centroid is defmed as the centre of a cloud of points.

2. Complete linkage clustering: The dissimilarity between two groups is equal to the greatest dissimilarity between a member of cluster x and a member of cluster y. This method tries to generate very tight clusters of similar cases.

3. Single linkage clustering: The dissimilarity between two clusters is the minimum dissimilarity between members of the two clusters. This method generates long loose chain clusters.

Identifying the optimum number of clusters is one of the biggest problems in Cluster Analysis. During fusion process, increasingly dissimilar clusters must be fused, i.e. the classification becomes more and more artificial.

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Cluster analysis classlftes a set of observations into two or more mutually exclusive unknown groups based on combinations of properties. A variety of clustering algorithms have been developed. These include: 1. Average Linkage clustering 2. Complete. Linkage clustering 3. Single linkage clustering

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3.6.3 Neural network Neural network is a massively parallel distributed processor made of simple processing units, which has a natural propensity for storing experimental knowledge and making it available for use. In other words Artificial Neural Network (ANN) is an adaptive computer program or an iterative numerical technique that facilitates solutions to different types of problems including pattern recognition and classification of data. They are so named due to their analogy to biological neurons and their connections. In the neural network approach, relationships between input and output variables are developed through a training process in which sets of inputs are applied to the network and the resulting sets of outputs are compared with the known correct values.

Neural network models are classified by the network topology, node (artificial neuron) characteristics and learning law. In a nutshell, the architecture of the network determines the input of each neuron.

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Type of activation function used, utilized output range of the activation function, type of bias (excitatory or inhibitory) basically determines the node characteristics. Activation function facilitates mapping of a wide range of linear and non- linear inputoutput relations for the targeted process. The most common activation function used in the neural network literature is the hard limiter, threshold, sigmoid functions, radial basis functions or combinations thereof The learning rules determine how the network will respond when an unknown input is presented to it. Therefore, the function of a neural network is determined by these parameters.

There are seven major components which make up an artificial neuron.

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Weighting Factors: A neuron receives many simultaneous inputs and each input has its own relative weight which in turn provides the input specific impact.

Summation Function: Here the processing element's operation is to compute the weighted sum of all of the inputs.

Transfer Function: In the transfer function the summation total can be compared with some threshold to determine the neural output to so that the processing element generatessignal.

Scaling and Limiting: After the processing element's transfer function, the result may be required to pass through additional processes which scale and limit the signal.

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Output Function: Each processing element is allowed one output signal which it rnay output to hundreds of other neurons.

Error Function: The difference between the current output and the desired output is called raw error. This is then transformed by the error function to match particular network architecture.

Learning Function: The purpose of the learning function is to modify the variable connection weights on the inputs of each processing element according to some neural based algorithm.

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Artificial Neural Network (ANN) is an adaptive computer program oran iterative numerical technique that facilitates solutions to different types of problems including pattern recognition and classification of data.

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Artificial Neural Network (ANN) is an adaptive computer program oran iterative numerical technique that facilitates solutions to different types of problems including pattern recognition and classification of data.

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Artificial Neural Network (ANN) is an adaptive computer program oran iterative numerical technique that facilitates solutions to different types of problems including pattern recognition and classification of data.

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There are Two Ways to Teach ANN 1. Supervised Training.

In supervised training, both the inputs and the outputs are provided. The network then processes the inputs and compares its resulting outputs against the desired outputs. Errors are then propagated back through the system, causing the system to adjust the weights which control the network. This process occurs over and over again as the weights are continuously tuned.

The set of data which enables the training is called the "training set." During the training of a network the same set of data is processed many times as the connection weights are ever refined.

2. Unsupervised or Adaptive Training The other type of training is called unsupervised training. In unsupervised training, the network is provided with inputs but not with desired outputs. The system itself must then decide what features it will use to group the input data.

This is often referred to as self-organization or adaptation.

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3.6.4 Pattern Recognition (PR) Etymologically, recognition means act of thinking again i.e ., it involves identifying and acknowledging. Cassiopeia a beautiful constellation at the edge of the Milky Way has definitely the shape of a "W". The character "Wâ&#x20AC;&#x153; formed in the sky by the collection of stars is nothing but pattern recognition by human brain. The interest in ANN started mainly due to difficulties in dealing with problems in the field of speech, image processing, natural language processing and decision making using known methods of pattern recognition and hence the field of ANNs gained prominence. Basic ANN models for PR problems are feed forward, feedback and competitive learning networks. They may be trained through supervised or unsupervised learning paradigm. PR task they perform may be one of the following: Pattern Association, Pattern mapping, Temporary Pattern Storage, Pattern Storage, Feature Mapping.

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Pattern classification, Auto-association, Pattern Environment Storage, Pattern Clustering and

Basic ANN models for pattern: recognition problems are: 1. feed forward 2. feedback 3 Competitive learning networks

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3.6.5 Wavelet Transform Fourier theory clearly indicates that a signal can be expressed as a Fourier expansion that is the sum of a, possibly infmite, series of sine and cosines.

The disadvantage of a Fourier expansion is that it has only frequency resolution and no time resolution. An AE signal has components in frequency and time domains. Thus, application of only frequency transform is not adequate to characterize the signal completely. Solution of this problem has been developed which is more or less able to represent a signal in the time and frequency domain at the same time. The wavelet transform or wavelet analysis is¡a recent development which tends to overcome the shortcomings of the Fourier transform. In wavelet analysis one uses a fully scalable modulated window to solve the signal-cutting problem. The window is time shifted along the signal and for every position the spectrum is calculated. This process is repeated varying the window interval for every new cycle. The result at the end is a collection of time-frequency representations of the signal, all with different resolutions. Because of this collection of representations we can speak of a multi-resolution analysis. In the case of wavelets, we speak about time-scale representations, scale being in a way the opposite of frequency, because the term frequency is used in Fourier transforms.

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The disadvantage of Fourier expansion is that its has only frequency resolution and no time resolution. By wavelet analysis one get the timefrequency representations of the signal, all with different resolution.

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3.6.6 Statistical Analysis The randomness inherent in the generation of AE, the uncertainties in the paths and the wave modes during its transmission from source to sensor and the instrumentation errors in quantifying the signal parameters all necessitate a statistical analysis of AE signals. One type of statistical analysis widely used in recent years is distribution analysis. In distribution analysis, some parameter of a burst emission signal is measured. This parameter is divided into a range of values in either a linear or logarithmic scale. As each signal is measured a counter assigned, to that particular range of values for the parameter, is increased by one unit. For example, if the parameter is peak amplitude, the amplitude may be divided into the dB bins and a signal with 43 dB peak amplitude would increase the counter assigned to the 43 dB bin by one unit. After many burst signals have been measured, the resulting counts can be plotted. A typical way of representing this data is to plot each point as a total number of all signals with that value or higher value of the parameters.

This summation curve is shown in Fig. 3.13.

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Fig. 3.13 Two methods of plotting peak amplitude distributions. The bottom curve (A) is the differential plots while the top curve (B) is the plot of total number of events with amplitudes greater than the amplitude at the point.

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The most common parameter used in distribution analysis is the peak amplitude; although signal energy, counts, duration are also used. Any signal, which can be measured, is used in distribution analysis. The main problem in the use of distribution analysis is not in the acquisition ofthe data but in its interpretation. The simplest type of analysis is to take distributions, such as the one shown in Fig. 3.12 during the course of an experiment and to look for the appearance or disappearance of features such as peaks, as the experiment progresses. This can be a very useful type of analysis. For example, the appearance of a number of high amplitude or energy events in a test of a fibre composite may signify a growing crack. Distribution analysis can be very useful in a real time test. By looking for changes in the differential curve, one can find the change in the source mechanism. However, the real jump would appear when computer based programs can be developed which would track changes in the distribution constants in real time as the tests progress.

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The parameters used in statistical analysis are the peak amplitude, signal energy, counts¡ and duration The main problem i'n the use of distribution analysis is its interpretation. Di'stribution analysis can be very useful in a real time test.

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3. 7 Acoustic Emission Test Systems One of the most important applications of acoustic technique emission is the monitoring of the integrity of large structures, such as pressure vessels and storage tanks. Monitoring of these large structures in realtime requires multichannel computer aided systems. Systems with up to 128 channels are available. The vast amount of data, data processing, and analysis required for real-time monitoring of a structure makes the use of a computer essential.

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Factors affecting the test equipment selection include:  Material to be tested - Homogeneous or heterogeneous (example composites) - Good or poor emitter - Attenuation of AE signals  Environment - Laboratory specimen - High temperature/radiation - Corrosive  Size and shape of the test object - Laboratory specimen - Structure - Bridge, Vessel, Pipe. If vessel or pipe - Pressurized with liquid or gas - Wall thickness-greater than 10 mm

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ď Ž Information desired - Location of AE source-defect, leak - Structural integrity - Advance notice of detrimental changes - Material properties ď Ž Location and nature of emissions - Welds, nozzles, fittings, manholes - Gage section on laboratory specimens - Burst or continuous emissions

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3.7.1 Real Time Monitoring System (RTMs) The real time acoustic emission monitoring system detects and locates dynamic or propagating flaws under the application of suitable stresses or loads in structural components during structural testing or in-service inspection. Such systems are versatile and can be used both in the field for monitoring proof tests and in the laboratory for following the deformation of atensile specimen. All the data obtained can be processed and displayed in real time. The system will detect and locate the potential areas such as growing cracks and defects, loose joints, structural yielding and leaks. The RTM can also be used as a process monitor to establish the mechanical performance of rotating machinery or other dynamic process equipment. The desirable features of a RTM System are:

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 Real time processing and display not only of acoustic emission source locations but also AE data versus parametric information.  Discrimination and suppression or elimination of background and spurious noises using suitable circuits.  Should be capable of accepting both 'Burst Type' and 'Continuous Type' acoustic emission signals and parametric (e.g. pressure, temperature, strain etc.) data inputs for display and correlation to test conditions like load, stress etc.  Flexibility to have single sensor or multiple sensors depending on the nature of the application. For example, tensile testing may require single sensor while multiple sensors would be needed for source location on a structure.  One system should cover tests in different facilities or different parts of the same structures with random sensor spacing and locations.  Sensors and preamplifiers should have the highest signal to noise ratio and provisions should exist for adjusting the gain ( 40 to 100 dB or to 120 dB) and bandwidth.  Automatic restart in case of power failures.

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 Single or multiple video displays to present the AE data in real time for the operator. Camera pictures from the scope may be obtained by the operator, either in real-time or post test.  Provisions to store all raw data.  Availability of software programs for a wide variety of processes, analysis and display modes for both real-time and post-processing.  Permanent data record formats suited to individual customer requirements.

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The real time acoustic emission monitoring system detects and locates the potential, areas such as growing cracks and defects (loose joints,:structural yielding and leaks),

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3.7.2 Computerized AE Systems Nearly all modem acoustic emission systems use microcomputers in various configurations as determined by the system size and performance requirements. In typical instruments, each acoustic emission signal is measured by hardware circuits and the measured parameters are passed through the central microcomputer to a disk file of signal descriptions. The customary signal description includes the: ■ threshold crossing counts (ring down counts), ■ amplitude, ■ rise time and often the ■ energy of the signal, along with its ■ time of occurrence and ■ duration and the values of slowly changing variables such as load and background noise level. During or after data recording, the system extracts data for graphic displays and hardcopy reports. Common displays include history plots of acoustic emission versus time or load stimulus, distribution functions, cross plots of one signal descriptor against another and source location plots.

Installed systems of this type range in size from 4 to 128 channels.

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The first generation of AE instrumentation (Fig.3.14) was constructed from hardware modules put together into a 19-inch rack bin and connected to a standard off- the-shelf minicomputer through a house-bus. The major disadvantages of such a system besides the high cost were poor bus structure for channels coordination and system lockout due to processing of a single AE source at a time. In addition to the above, the low speed of data acquisition made such a system inadequate for FRP applications.

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Fig. 3.14 First generation of AE Computerisation

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In the second generation of AE instrumentation (Fig. 3.15) some of the earlier limitations were overcome based on microprocessor technology. For the first time, an attempt was made to build a computer based system. One of the most important improvements made was the separation of the functions of data acquisition (front end processing) from that of display, storing and arithmetic calculations. Thus, two microprocessors were used simultaneously.

This made such a system more suited for the AE applications in early eighties.

IEEE 488 bus structures enabled the system designers to be able to store data at a rate greater than 500 hits/ sec, using a system with Direct Memory Access DMA concept. In addition, graphic monitors were part of the sma.rt computer terminal. This led to faster real time displays and usually several were available simultaneously. Faster data acquisition and processing through parallelization of computing elements and incorporation of advanced signal analysis capabilities were the improvements incorporated in the third generation systems (Fig. 3 .16). This resulted in considerably enhanced system performance. The third generation AE multi channel systems are capable of detecting, storing and displaying simultaneously thousands of AE hits Charlie Chong/ Fion Zhang

(50000/sec). Unlike this parallel processing computer system, conventional computer systems provide one central processing unit (CPU) that processes one stream of data and instructions. Such computers, as discussed previously, are too slow for real time AE needs. Thus the computer power can be increased through high parallel processing, designing the main AE computer from a group of single board computers (parallel computing elements) that share data processing tasks (one per channel or two) and effectively coordinate with another single board computer. The modules (single board microcomputer) are self-sufficient; that is they contain all the information they need to process data and transfer control to and from the local host computer. The host computer divides the task of storing, displaying and data manipulation among the powerful microprocessors. Such systems are being used for multitasking field operations.

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Modern acoustic emission systems use microcomputer in various configtrrations, The customary signal description include the threshold crossing counts (ring down . counts) amplitude rise timeand. often the energy ofthe signal, along wjth its time of occurrence and duration and the, The value of slowly changing variables such as load and background noise level.

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Fig. 3.15 Second generation of AE computerization

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Fig. 3.16 Third generation of AE computerization

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Q 1. The main function of a pre amplifier is a. to enhance signal level against noise b. to isolate background noise c. to isolate electrical interference during a AE testing d. none of the above Q 2. 1000 times amplification of a signal means . a. 40 dB b. 10 dB c. 100dB d. 60dB Q 3. The term 'Ring down count' refers to a. the number of events from a source b. the number of times a signal crosses a preset threshold c. the number of sensors required to perform a test d. none of the above 4. The acoustic emission signal

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Q 4. The acoustic emission signal amplitude is related to a. the preset threshold b. the band pass filters c. the intensity of the source d. background noise level Q 5. Planar source location is useful for locating source in a. complex structures b. pipelines c. flat plates, cylinders and sphere d. heterogeneous materials

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Q 6. An AE source is considered to be active a. if its counts continue to increase with increasing or constant stimulation. b. if its counts remains constant with increasing constant stimulation. c. it its counts decrease with increasing or constant stimulation. d. none of the above.

Q 7. Distribution plots are useful a. for intensity measurement of a source. b. to identify and classify different type of sources existing simultaneously. c. to locate source in linear mode. d. to locate source in complex structures.

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Chapter 4 Sensor Calibration and Source Location P. Kalyanasundaram, C.K. Mukhopadhyay, S.V Subba Rao Series Editor: Baldev Raj B. Venkatraman

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This chapter deals with two of the most important topics namely (1) sensor calibration and (2) source location.

Sensors are the heart of any AE system. Prior to any AE measurement either in the laboratory or in the field, it is very essential that the sensor to be used for experimentation be calibrated. Sensor calibration ensures reliability of data acquired and thus provides measurement confidence to the user. It also helps in ensuring adequacy of sensor performance and helps detect degradation or variations in output if any, with time. It also allows more meaningful data comparison/ interpretation.

For meaningful evaluation by any NDT method, apart from detecting the flaws and defects present, it is very essential that the location of the flaw be known so that corrective measures such repairing the flawed area or monitoring it in subsequent campaigns be undertaken. The various sections below dwell extensively on the steps involved in sensor calibration, the various types of standard AE sources that can be used for calibration and the sensor positioning and techniques that can be used for accurately locating the source within a structure in AE measurements.

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4.1 Absolute Calibration of AE Sensors The calibration gives the frequency/sensitivity curve for a given (test) sensor, i.e. Frequency response of the sensor to the acoustic waves. The calibration is made using an input source as a well established reproducible dynamic displacement normal to the mounting surface. The response of the test sensor is compared against a standard sensor and the performance evaluated and calibration graph established. The unit of the sensitivity is output voltage per unit mechanical input (displacement, velocity, or acceleration). Fig. 4.1 gives the general arrangement for calibration. Outlined below are the general steps for calibration of AE sensors.

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Fig. 4.1 Setup for calibration of Transducers

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4.2 Steps for Calibration A standard source is normally used for calibration. There are many standard sources available for sensor calibration.

These are (i) the gas jet impinging on a solid surface (ii) electric source applies periodic spark to the structure (iii) pulsed laser beam on surface (iv) ultrasonic transducer (v) breaking of glass capillary or pencil lead on the surface of a structure.

In this particular case, we use the acoustic impulse generated by breaking of a capillary tube on the surface as the source. Two sensors are used; a standard sensor and the test sensor to be calibrated.

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1. Place the standard dynamic source at the centre of the test block shown in Fig. 4.1.

2. Mount the standard sensor and the test sensor at equal distances from the standard source. Ensure that both are well coupled acoustically to the surface of the test object.

3. Ensure that all instrumentation and data recording setups are in place and all interconnections are made.

4. Break the capillary glass. Breakage of a glass capillary is used as a standard source, which releases a step friction force with rise acoustic waves time of 0.1 ms.

5. Record the voltage response from the standard source by the two sensors.

Figures 4.2 show the voltage response from standard and test sensors respectively from a typical standard source. The transducer voltage response is determined at discrete frequency intervals in the range approximately 1 0 kHz up to 1 MHz.

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Fig. 4.2 Typical voltage- time response from different sensors

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6. Obtain the fast fourier transform of the voltage response of the sensors. A typical FFT of the voltage response given in Fig. 4.2 is shown in Fig. 4.3.

The frequency response of the test sensor as calibrated against the standard sensor can be obtained by division of the ordinates of the frequency spectrum of test sensor with the corresponding ordinates of the standard sensor and is shown in Fig. 4.4. standard sensor

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test sensor

Fig. 4.3 Magnitude spectrum of standard sensor (a) and test sensor obtained

by FFT of data

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Fig. 4.4 Calibration curve for test transducer

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Fourier Transformation

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http://groups.csail.mit.edu/netmit/sFFT/algorithm.html

Fourier Transformation

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http://groups.csail.mit.edu/netmit/sFFT/algorithm.html

Fourier Transformation - A fast Fourier transform (FFT) is an efficient algorithm to compute the discrete Fourier transform (DFT) and its inverse. There are many distinct FFT algorithms involving a wide range of mathematics, from simple complex-number arithmetic to group theory and number theory; this article gives an overview of the available techniques and some of their general properties, while the specific algorithms are described in subsidiary articles linked below.

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https://www.withfriendship.com/user/Athiv/fast-fourier-transform.php

The calibration yields the frequency response of a transducer to waves, at the surface, of the type normally encountered in acoustic emission work.

Comparison can now be made between various sensors with respect to the frequency response by using the curves obtained for various sensors using the procedure described above.

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Sensors are the heart of any AE system. The calibration gives the frequency/ sensitivity curve for aâ&#x20AC;&#x2DC; given (test) sensorr i.e. frequency response of the sensor to the acoustic waves.

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4.3 Sources for Calibration of Sensors and Test Systems 4.3.1 Ultrasonic Transducer Repeatable broad frequency acoustic waves can be produced by using a heavily damped ultrasonic transducer. The centre frequency of the transducer should be around 2.25 to 5 MHz. The diameter of the active element should be at least 12.5 mm to provide measurable signal strength at the position of the test sensor.

Keywords: broad frequency acoustic waves can be produced by using a heavily damped ultrasonic transducer.

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The characteristics of the piezoelectric sensor used as a simulator include: 1. The sensor should give out signal with wide frequency range.

2. Repetition rate should be such that the time between the pulses is not less than the maximum distance between the detecting sensors divided by the sound velocity, plus the signal duration and the computer process time.

3. The rise time of the simulator signal should be shorter than the period for maximum frequency of the signal generated by the simulator (i.e. 1/ fmax).

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4.3.2 Gas Jet Suitable gases for this apparatus are extra dry air, helium etc. A fixed pressure between 1.5 to 2 Kg/cm2 is recommended. This can be fixed to a test block as shown in Fig. 4.5 for calibrating the sensors (or) can be moved on the structure for calibration of various channels to check the uniform sensitivity of individual channels and accuracy of source locations.

4.3.3 Pencil Lead BreakA repeatable acoustic wave can be generated by carefully breaking a pencil lead against the test block. When the lead breaks, there is a sudden release ofthe stress on the surface of the block where the lead is touching. Hsu-Nielsen pencil lead break arrangement is shown in Fig.4.6.

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Fig. 4.5 Gas - jet test blocks

(A) Opposite surface comparison setup (B) same surface comparison setup Charlie Chong/ Fion Zhang

Fig. 4.5 Gas - jet test blocks

(A) Opposite surface comparison setup (B) same surface comparison setup

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Fig. 4.6 Hsu-Nielsen source

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4.4 Location Techniques When the AE signals are transmitted, it is easy to use timing techniques for source location. The technique can be used if a particular characteristic of the signal detected by the sensors in the array is identified. In that case, the time delay of the signal, based on the identified characteristic, between two or more sensors in the array provide in location of the source.

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The fundamental premise in locating sources of acoustic emission provides an advantage over the other non-destructive testing methods. There are many ways of deducing the location ofacoustic emission activity from the electrical output of the sensor-amplifier chain. Some of thetechniques are as follows: • Linear location • Planar location - Triangular arrays - Flat plates - Cylinders - Spheres • Zone location - Complex structures - Heterogeneous materials • 3 dimensional - Analytical (Multi-channel, Redundant solutions) - Zone calibration (adaptive)

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4.4.1 Linear Location Consider a case where two sensors are mounted on a linear structure such as a pipeline shown in Fig. 4. 7. In this case, the time difference and the order of arrivals is used to locate the source. Let the source be at a distance 'd' from sensor S1 and the distance between the sensors S1 and S2 is 'D'. The time difference between the arrival of the signal at the two sensors is ∆t = (t1 - t2 ) and the wave velocity is V, then the source location 'd' is given by: d = ½ (D - ∆t·V) Leak detection in pipeline is an example of utilizing this method of source location. If the source is outside the two sensors, then ∆t always corresponds to the time of flight between the sensor pair. The hit sequence can be used to infew whether the source is at or beyond the location of the first hit sensors.

The minimum number of sensors required for two-dimensional location of the source is three. (?)

Linear- 2 Plane – 3 3 dimensional -5 Charlie Chong/ Fion Zhang

d = ½ (D - ∆t·V)

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In two-dimensional case where the source is known to be located in a plane, the difference in distance traveled by the wave to a pair of transducers can be calculated from the measured time difference. For a given pair of transducers, the known fire difference value â&#x2C6;&#x2020;t can be anywhere on a hyperbola. For the same source with another pair of The minimum number of sensors required for two-dimensional location of the source is three. transducers a second â&#x2C6;&#x2020;t value can be obtained which again traces a hyperbola. At the point of intersection of these two hyperbolae, there exists the AE source. This type of location in a plane is termed as planar location. The minimum number of sensors required for two-dimensional case is three. Consider two sensors mounted on an infinite plane and assume the ideal condition where the stress wave propagating from an acoustic emission source that travels at a constant velocity in all directions. Refer Fig. 4.8.

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The time difference (â&#x2C6;&#x2020;T) and the order of arrivals is used to locate the source Fig. 4. 7 Linear location methodology

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Fig. 4.8 Result of source location with two sensors in an infinite plane

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∆tV = r-R Z = R sin 8 Z2 = r12- (D-R cosθ)2 R2 sin2θ = R2 - (D - R cos θ)2 R2 = r12 - D2 + 2 DR cos θ Subtracting r1 = ∆tV + R from equation (1) yields

R = ½ (D2- ∆t2V) / (∆tV +D cos θ)

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Equation 6 is for a hyperbola passing through the source location (x.y). Any point on the hyperbola satisfies the input data (the hit sequence and the time difference measurement). Fig. 4.7 is insufficient for the two dimensional source location requirements. However, the addition of a third sensor, as shown in Fig. 4.9 will give another hyperbola. The input data now include a sequence of three hits and two time difference measurements (between the first and second hit sensors and the first and third hit sensors).

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Fig. 4.9 Source location in two dimensional case

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Equations (7) & (8) can be solved simultaneously to determine the source location in two dimensions i.e., the intersection of these two hyperbola will give the location of the source. The planar location technique is particularly useful for the location of the source on flat plates, cylinders and spheres etc.

Different array geometries can be used. Usually triangular array and quad array are used. In triangular array configuration, three transducers are arranged in a triangle. The system operates to locate a flaw in the area covered by the triangle.

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But in three sensor triangular arrays, some times multiple solutions may confuse the exact source location. For the same delta 'T' value, the two hyperboles intersect at more than one point (Fig. 4.8). This problem can be overcome by using a fourth transducer. By using four transducers in a two sets of two-dimensional situations, one redundant data set is generated. Thus it can remove the multiple locations found in arrays when three transducers are used (Fig. 4.9). Fig. 4.10 gives the AE sensor location over the entire vessel in a flat projection. In this pattern, interlocking hexagonal arrays provide the input. The transducer arrival sequence is used to determine in which of the twelve possible sectors, the source was located (Fig. 4.11). After this sector determination, the value of delta 'T' is used to find exact location of AE source.

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Fig. 4.10 Multiple solution in case of AE source location using three transdueer arrays

Regions with ambiguous solutions exist near and 'behind' each transducer Charlie Chong/ Fion Zhang

4.4.2 Zone Location In acoustic emission technique, any method of flaw location or source location based on measurement of time differences and then by solving mathematical equations will face problems if the component under test is of irregular shape or with different types of discontinuities (which would attenuate, scatter or cause echoes of traveling stress wave). In many ractical cases, the structures to be monitored would belong to this category. But with microprocessor based and computer controlled systems entering this field in a big way, source location can be carried out on components of virtually any shape. Arbitrary array (Zone location) is one such method.

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Fig. 4.11 QUAD array for source location

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Sufficient number of sensors is fixed at any convenient random locations on the surface of the component to be monitored. The total surface area is divided into several zones. Each zone is defined by a set of zone boundaries.

A zone boundary comprises of a sequence of arrival of signals at different sensors, and the relative time difference delta 'T's. To get these zone boundaries, sources are simulated within the zone using a pencil lead breakage or tapping or impulse generated by a spark. Similarly, using artificial sources all zones are defined in terms of sets of zone boundaries. These sets are stored in the computer memory in the form of a look-up table. During the actual test whenever any source emits AE signals, the sequence of arrival at different transducers and the corresponding delta 'T' s are compared with the look-up table entries and the computer declares the zone from which the signal was generated. This method has an edge over the other array patterns in that the usual problems encountered like errors due to odd geometry of the structures, scattering at discontinuities etc. are automatically taken care of during the calibration procedure itself.

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For zone location, sufficient number of sensors are fixed at any convenient random locations on the surface. The total surface-area is divided into several zones. Whenever any source emits AE signals the sequence of arrival at different transducer locations and the corresponding time difference are compared with the look-up table entries and the computer declares the zone from which the signal was generated.

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4.4.3 Three Dimensional Source Location Most applications of acoustic emission source location techniques are directed at the problem of locating a source in a practically two dimensional shell type structures. However, when the wall thickness is substantial or when the area of interest lies internally in the shell, then locating a source in three dimensions becomes important. One approach is to extrapolate the twoimensional technique into three dimensions. Each sensor location is defmed in full spatial coordinates (X, Y and Z) and the hyperbola of equations for X and Y become surfaces. The solution is more involved in two dimensions and mapping on to a twodimensional surface will present its own set of problems.

Source location in the field of rock mechanics is the best example for this.

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4.4.3.1 Cross correlation technique for source location Conventional technique for measuring time differences between two burst type emission waves can not be used for continuous sources such as leaks.

However, cross correlation techniques can be used to measure the time difference or time delay of one wave from another for discrete waves as well as continuous waves. There is a relation between earth quake which happened at Sumatra and tsunami that hit Srilanka. One can correlate these two phenomena although they happened in very distinct time and place.

Cross correlation techniques have proved particularly useful in locating leak sites on pipes. For practical applications, the cross correlation function between an arbitrary wave A(t) and another wave B ( t + Ď&#x201E;) with a time delay Ď&#x201E; is given by

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where T is a finite time interval. This definition is different from the general definition, in which T approaches infinity. Equation (9) requires that the product of A(T) and B (t + τ) be integrated over the time t. Then if τ is considered as a variable, the cross correlation function RAB (τ) is function of τ.

In other words, for a given τj ,RAB (τj) is obtained from the integration of A(t)B(t) The cross correlation function is integration over a finite time period.

Furthermore, in practical applications, a finite portion of each wave is used for data sampling and the wave amplitude beyond the portion used is equal to zero.

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4.4.3.2 Signal amplitude method for source location With zone location method, it is only necessary to determine the sensor with the highest signal output and perhaps the next highest. The limitation of this method is that the zone may be unacceptably large. If the sensor outputs are not only rank ordered but also measured, the location of the leak may be determined with greater degree of precision, provided that the attenuation characteristics of the component are known. In order to use this amplitude method for continuous source location, it is necessary to measure the attenuation in detail for a particular application (data from similar structures may also be used). It is also important to recognize that sound attenuation in a structure is a complex phenomenon related to wave paths, wave modes and dispersion. The signal amplitude method for source location comprises of the following three stages.

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1. Determine the two sensors closest to the leak by noting the highest and second highest outputs from the array. It is necessary that the sensor pair should cover the leak zone ..

2. Determine the difference (in decibels) between the two sensor outputs and compare that with the attenuation characteristics of the component.

3. On a two dimensional plane, this process describes a hyperbola. A similar process is undertaken using at least one more sensor to generate an intersecting hyperbola. The intersection of hyperbola defmes the source.

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Chapter 5 Acoustic Emission Testing Procedure P. Kalyanasundaram, C.K. Mukhopadhyay, S.V Subba Rao Series Editor: Baldev Raj B. Venkatraman

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5.1 Introduction In this chapter, details on the steps involved in carrying out an acoustic emission test is discussed. It is always necessary to follow a written procedure in order to ensure that proper methodology is adopted, necessary precautions have been taken and also provide baseline data for future comparison studies. The same procedure should be followed whenever periodic proof testing, inservice for requalification, for example in the case of pressure vessel, is carried out. The test procedure is normally prepared by the organization performing the examination in consultation with the users and approved by the users. Any standards (like those prepared by ASTM, Acoustic Emission Working Groups in America, Japan, Europe) available may be consulted while preparing the procedure.

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The test procedure usually specifies the following: (i) The equipment to be used (ii) Choice, characteristic and placement of acoustic emission sensors.

The characteristics of the sensor to be furnished include: a. Type of sensor (resonant, wideband, single ended/differential). b. The type of electrical connectors c. Principal resonant frequency and bandwidth at -6 dB (?) d. Frequency-response curve e. Physical dimensions f. Piezoelectric element and coupling shoe used, directionality, response to different types of waves, resistance to environment, vibration, shock etc. (optional).

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(iii) Couplants to be used, including details such as couplant type. If a waveguide is used, then details of the waveguide. (iv) Attachment of the sensor to the structure (mechanical, magnetic method) (v) Nature and preparation of the contact surface (vi) Codes and standards to be followed (vii) The process for applying the stimulus to the component (viii) The data to be recorded and reported (ix) The qualification of personnel for operating the equipment and interpreting the results.

The procedure given here is basically applicable for monitoring structures such as pressure vessels, piping system, or other structures that can be stressed by mechanical or thermal means. However, the procedure can be adopted with suitable modifications for other application like online weld monitoring, fatigue crack growth studies etc.

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5.2 Codes and Standards in Acoustic Emission The list of various standards available on acoustic emission testing are given below: 5.2.1 List of ASTM Standards and ASME Documents • E569 Acoustic Emission Monitoring of Structures during Controlled Stimulation • E610 Definition of Terms Relating to Acoustic Emission • E650 Mounting Piezoelectric Acoustic Emission Contact Sensors • E749 Acoustic Emission Monitoring during Continuous Welding • E750 Measuring the Operating Characteristics of Acoustic Emission Instrumentation • E751 Acoustic Emission Monitoring during Resistance Spot Welding • E914 Test Method for Acoustic Emission for Insulated Aerial Personnel Devices • E976 Guide for Determining the Reproducibility of Acoustic Emission Sensor Response

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• E1067 Acoustic Emission Examination of Fiberglass Reinforced Plastic Resin (FRP) Tanks/Vessels • E 1106 Primary Calibration of Acoustic Emission Sensors • E1118 Acoustic Emission Examination of Reinforced Thermosetting Resin Pipe (RTRP) • E 113 9 Practice for Continuous Monitoring of Acoustic Emission from Metal Pressure Boundaries • E1211 Standard Practice for Leak Detection and Location using Surface Mounted Acoustic Emission Sensors

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ASME approved code case 1968 (approved in 1985) titled 'Use of AE Examination' in lieu of 'Radiography' specifies the conditions and limitations under which AE examination can be conducted during the hydrotest in lieu of radiography for examining the circumferential closure welds with wall thicknesses upto 2.5 inches in the pressure vessel.

Article 11 of ASME Section V entitled' AE Examination of FRP Vessels' provides the rules for applying AE to examine new and in-service FRP vessels under pressure, vacuum or other applied stress.

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Documents from European Working Group on Acoustic Emission (EWGAE) • Code I Acoustic Emission Examination- Location of Sources of Discrete Acoustic Events • Code II Acoustic Emission Leak Detection • Code ill Acoustic Emission Examination of Small Parts • Code rv Definition of Terms in Acoustic Emission • Code V Reconunended Practice for Specification Coupling and Verification of the Piezoelectric Transducers Used in Acoustic Emission Documents Issued by the Japanese Society for Non-Destructive Inspection (JSNDTI) • NDIS-2106 Evaluation of Performance Characteristics of Acoustic Emission Testing Equipment (1979) • NDIS-2109 Acoustic Emission Testing of Pressure Vessels and Related Facilities during Pressure Testing ( 1979) • NDIS-2412 Acoustic Emission Testing of Spherical Pressure Vessel Made of High Tensile Strength Steel and Classification of Test Results (1980)

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Documents on Personnel Qualification and Certification AE is an advanced NDE technique and the evaluation and interpretation of indications like in other NDE techniques requires appropriate background knowledge. AET personnel thus need to be qualified and certified. Some of the Indian and international standards pertaining to qualification and certification include IS- 13 805 (2004) -Indian standard, SNT-TC-1A of the American Society for NDT, EN473 of the European Federation of NDT and ISO 9712 of the International Standardisation Organisation. All these guidelines and standards have procedures for qualification and certification of AE personnel as Level I, Level II or Level III.

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5.3 Tasks before AE Testing 5.3.1 Information Needed Prior to Testing The following information is needed before proceeding with the tests: 1. The area( s) to be monitored: Many applications require an arrangement of sensors such that all the areas of the structure are monitored. In other applications, only a portion of the structure (i.e. weld region, nozzle region etc. which are prone to failure more commonly) needs to be monitored.

2. The stimulation type, parameters and stimulation time periods.

3. The ambient conditions during the testing.

4. The criterion to be used for interpreting, classifying and evaluation of AE indications.

5. Survey of the structure for detecting extraneous background noise that could preclude effective testing and identify possible means of elimination/reducing the noise. Detailed information on types of noise and the possible elimination procedure are given in section 5.10.

6. It is essential to prevent any catastrophe during stimulation. It is therefore necessary to identify the persons responsible for stopping or holding the test if unusual AE activity is detected.

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The AE testing should be carried out by qualified persons. Qualification can be based on levels, I, II, and III certificates issued by international bodies ISNT or ASNT or BINDT or any other accredited organization acceptable to the users. It can also be based on demonstrated skill, training and experience as agreed to by the users.

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5.3.2 Preparations needed Prior to Testing Before actual examination starts, the following preparations are to be made: 1. Determine the type, number and placement of sensors. This requires knowledge of both material and physical characteristics of the structure and the features of the instrumentation. The strength of AE activity from the material, attenuation of the signal within the material, accuracy and precision of detection and location needed decide the sensor placement distance and also the inter-sensor distance. The background noise may control the frequency range of the sensors to be used.

2. Establish communication between the control room point for the application of the stimulus and the AE test control centre.

3. There should be provisions for continuous recording of the measure of stimulus.

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4. It is necessary to identify extraneous noise sources such as vibrations, friction and fluid flow and eliminate them by acoustic isolation or control.

Various methods of noise elimination/ reduction are discussed in section 5.12.

5. The sensors are to be attached to the structure and acoustically coupled.

The ASTM specifications E-650 can serve as guideline for this.

6. The AE system including the sensor is to be calibrated. The procedure for calibration is given below. The calibration should be carried out at least once before and after the test. Additional calibration may be performed during the test at the discretion of the users.

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5.3.3 Calibration of Sensors Since there is a possibility for variation in sensor characteristics with time, operation and environmental conditions, sensors should be checked for (i) frequency response and (ii) sensitivity, periodically and as part of the procedure. A reproducible artificial reference AE source should be used for checking the sensors. The possible sources are (i) brittle fracture source or pencil lead (Hsu-Nielson) break source (ii) gas jet source. These can be established very easily in the laboratory or field. The placement of source and sensor should be appropriate while calibrating the sensors. The procedure and steps involved in the calibration of test sensors is given below.

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5.3.4 Checking Coupling Sensitivity The ratio of the output voltage of preamplifier when a simulated signal is received to that of the noise level voltage should be constant for an the sensors to make sure that the coupling efficiency is same for all the sensors.

Then, the post amplifier gain of each channel should be adjusted such that the peak voltage is same for all the sensors from a given simulated source. It is advisable to use a simulator giving out wide frequency signal, like helium gas jet.

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5.3.5 Calibration of the Test Equipment Like the sensor, the measuring system should also be verified for its reproducible performance. Hence, checks are carried out on the entire assembly for (i) checking adequacy of mounting arrangement by ensuring reproducibility of the sensor signal by repeatedly removing the sensor and remounting.

The difference in relative sensitivity among various channels should be less than 3dB. (ii) correct working condition of the measuring system and (iii) confirm that the experimental conditions are similar to the previous testing when AE examinations are carried out periodically, like proof testing of pressure vessels for requalification etc. Therefore the checking procedure described below is quite critical as it forms the basis for quantitative evaluation of the AE data obtained during the performance of any AE examination.

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Calibration of an AE test system is performed by placing a standard AE source simulator at a specified distance from a single sensor or from the sensors in an array. The simulation which can be used for calibration of the sensors can also be used for calibration of the complete system. A piezoelectric sensor is commonly used as an impulse simulator, which is most ideal for calibration of source location. The simulator sends out a signal of constant amplitude. A constant output is more important than the actual amplitude.

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The simulator can be used during the calibration of the AE testing system for the following: 1. Check the gain of various sensor-pre-amplifier, post-amplifier assembly of each channel.

2. Measure the velocity of Rayleigh waves in the structure.

3. Using the velocity data as input, confirm the location of AE sensor positions.

4. Estimation of attenuation characteristics of the material through which the AE signal travels. This should help in deciding sensor placement distance.

5. Verify the operation of complete system.

6. Check the reliability of the source location software.

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(a) Sensitivity Calibration of Individual Channels For flaw detection and location applications, it is recommended that all the channels in an array be adjusted so as to be equal in sensitivity to withinÂą 3 dB. The simulator should also check and confirm the location accuracy to within Âą5% of the distance between the location array sensors or to within the distance of (a?, two?) wall thickness of the structure being tested. AE simulator is located at a known distance from the sensor. The individual channel response to the stimulated emissions must be monitored and the sensitivity for each individual channel adjusted based on the background noise or other sources. Each change in sensitivity shall be recorded.

Âą3dB Charlie Chong/ Fion Zhang

5.4 Examination Procedure After calibrating the sensor and the test system, AE examination of the structure can be undertaken. Acoustic emission data can be accumulated during or after the stimulation of the structure or both. By analysing the difference in the time of arrival of AE signals at multiple sensors, the AE source locations can be determined.

The data may be accumulated over a specified parametric range. The parameters are pressure, stress, temperature, time etc. As the stimulus is applied, the number and location of the emitting sources and the amount of AE activity detected from each source is recorded. The AE rate at one or more sensors may be monitored and displayed in real time during stimulation.

If the AE activity, intensity or similar AE quantity shows an increase such as to cause concern, notify the users of the structure are notified. It shall be users' decision to reduce, hold or stop the test.

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The cause of the AE increase shall be investigated before continuing the stimulation schedule. It may be noted that continuous emissions from any leak in a structure stimulated by pressure may mask AE from sources close to the leak. It is therefore essential to plug all the leaks. After completing the test, the calibration should be repeated as described earlier. Any changes in the resulting gain settings or in source locations performance relative to previous calibrations should be recorded and one should make appropriate compensations during interpretation of the data.

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5.5 Exan1ination of the Records All calibration data and instrument adjustment including equipment description and performance data shall be included in the records of the examination and signed by the responsible personnel. The information recorded should be sufficient to permit complete reanalysis of the results. The following minimum information should be available in the records: 1. Material and physical characterization of the structures.

2. Sensor specifications, including size, sensitivity, frequency response, method of attachment, type of couplant, type and length of connecting cables.

3. Sensor locations.

4. Functional description of signal conditioners, processors and read out equipment.

5. Schedule, procedures and results of all calibrations.

6. Method of stimulation and examination details.

7. Raw and processed data.

8. Permanent data record of the measured AE parameters, in analog or digital form.

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5.6 Data Display Once the signals are detected based on the threshold criterion, the signals are processed and the data can be displayed in various forms to enable defect detection, location and evaluation. Easy detection and interpretation of the onset of a catastrophic situation should be possible from the method of data display so that quick remedial actions could be taken. The data display should meet the following requirements:

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1. Location of AE sources in real time.

2. Test variables, like pressure, temperature, stress etc. should be displayed against time to monitor the changes in stimulus.

3. The display should be updated as and when new data is acquired. There are various types of display modes available.

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Typical displays and their usage are as following: 1. Test Parameter (Vs) Time (Fig. 5.1). The test parameter can be pressure, stress, temperature etc. This shows how the stimulus is increased, decreased, and held constant with time.

2. Cumulative AE sources (events/ring down counts) versus time (Fig. 5.2) is the most familiar way of presenting the AE data. This plot establishes the total activity from the structure/ component independent of test parameter.

3. Rate at which new sources become active versus time (Fig. 5.3). This type of display is useful for example, when the test parameters are not constant with time and can provide an early warning of failure.

4. RMS signal level vs Time (Fig. 5.4). This measures the root-mean-square voltage level of the AE signal coming from a sensor. It is independent of threshold level. Higher RMS voltage from incorrect weld bead compared to correct weld bead it shown in Fig. 5.4. It can provide information as to when yielding of a material occurs. It also gives presence of any leak and the information on total system noise.

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Fig. 5.1 Variation in Stimulus with Time

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Fig. 5.2 Variation in total AE counts with time

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Fig. 5.3 Variation in AE counts rate with time

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Fig. 5.4 Variation in AE counts rate with time RMS signal level vs Time?

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5. Total AE activity versus test parameter such as pressure in shown in Fig. 5.5. This is important if the test has hold periods since increases in the amount of AE data produce increase in the height of the curve. When AEactivity continues to increase with no increase in pressure, this usually indicates that the structure is deteriorating and that the crack may be approaching a critical size. At critical size, the crack suddenly speeds up and spreads across the material or structure. Fig. 5.5 has been explained with more details in section 5.6.2.

6. Rate at which AE activity is generated versus test parameter, such as pressure as shown in Fig. 5.6. This display is useful in the analysis of the AE data when the test pressure is continuously increased. A 'rate' display is frequently easier to interpret in tests in which there is a large number of total events.

7. Display of sensor arrays and AE source location with respect to sensors depicted in Fig. 5. 7. Each AE event will produce one unique point of light on the display, positioned as .to its location on the test structure. This will immediately indicate the progress of AE activity, i.e. severity of flaw, growth etc.

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Fig. 5.5 Variation in total ringdown counts with stimulus

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Fig. 5.6 AE counts per cycle vs. pressure

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8. Amplitude distributions, derived from the first sensor to receive the data for each valid event. This has already been explained in the earlier chapters.

This display has one of its most useful features, the ability to quickly shown that the threshold are properly selected or not can provide a mean of quickly ranking the AE sources with respect with their activities. This can also be used to ascertain if there is any changes in source mechanism causing the failure.

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5.7 Interpretation of Results All results must be summarized as an appropriate layout map, displayed or tabulated or both for ready reference and interpretation. This should also display the location and classification of each source with pertinent comments.

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Fig. 5.7 Video display of sensor arrays and' AE source location in respect to sensors

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5.8 Source Location All location data results shall be presented in a manner consistent with previously calibrated location accuracy. The source location techniques are detailed below.

5.8.1 Source Classification Sources are usually classified with respect to their acoustic activity and intensity. Acoustic activity of a source is normally measured an event count or emission count. ď Ž A source is considered to be active if its event count or emission count continues to increase (at a constant rate?) with increasing or constant stimulus. ď Ž A source is considered to be critically active if the rate of change of its count, or emission count, with respect to the stimulus, consistently increases with increasing stimulation or if the rate of change of its event count, or emission count, with respect to time, consistently increases with time under constant stimulus (Fig. 5.5).

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Preferred intensity measures of a source are: ■ average detected energy per event, (VRMS or MARSE) ■ average emission count per event, (Nc?) ■ average amplitude per event. (Ā?)

A source is considered to be intense if it is active and its intensity measure consistently exceeds, by a specified amount, the average intensity of active sources.

The intensity of a source can be calculated for increments of the stimulus or of events. An intense source is considered to be critically intense if its intensity consistently increases (rate of increase?) with increasing stimulus, or with time under constant stimulus (Fig. 5.8). It is noted that if there is only one active source, the intensity measure of the sources is the average intensity of all sources, and therefore the intrinsic comparison is no longer applicable. In that case, it is necessary to classify the source through comparison with results from similar tests. In addition to activity and intensity, another characteristic of each detected AE source that should be considered for source classification is the size of the "region" of the located source.

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Fig. 5.8 Evaluation of sources

Critically active Intensely active active inactive

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The clustering of the located events from a sharp discontinuity, such as a crack, is usually dense, while regions of plastic deformation associated with, for example, corrosion pits, result in source areas that show more uncertainty in the definition of their size, the events being contained rather sparsely in the region. In most cases, a growing crack is considered to be the more serious defect. However, activity and intensity may not suffice for distinguishing between the two. Normally, there is subjective judgment on what size of location bundle or cluster constitutes an isolated source.

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The Clustering Of The Located Events

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5.8.2 Source Evaluation Sources are usually evaluated by their activity or intensity. Sources considered to be critically active, critical intense, or both are indicative of questionable structural integrity and, if possible, should be evaluated by other NDT methods. Sources considered to be intense are indicative of possible flaw growth and, if possible, should be evaluated by other NDT methods.

Sources considered to be active but not intense shall be recorded for comparison with sources detected during subsequent examinations. Sources considered to be of low activity and intensity are not usually required to be further evaluated or subsequently correlated.

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5.9 Comparison with Calibration Signals As already explained, calibration signals are used to adjust the sensitivity of all sensors to the same level, and to determine the attenuation in the test structure. Since the AE instrumentation has been calibrated earlier, the AE signal data obtained can be compared with calibrated signals to know the severity of flaw and source characteristics. Also, when we use the same calibration procedure during future tests, the present data can be compared with future data to determine whether there is any flaw growth, etc.

5.10 Source Verification and Evaluation by other NDT Methods Once an active AE source (flaw) has been detected and located during AE examination, the confirmation and quantitative estimation ofthe flaw can be made using other NDT techniques, such as ultrasonic, radiography, magnetic particle testing, liquid penetrant testing etc. depending on the component, accessibility, type of flaw expected and the location of the flaw.

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5.11 Test Report The reports of AE tests are evidence of the condition of the structure as determined by the test condition at a particular time. They provide the user of the structure and a regulatory agency with documentation as to the existence of discontinuities. It is recommended that critical structures be subjected to a "base-line" inspection prior to putting them into service. Periodic inspection and proper documentation helps to monitor the discontinuities and take appropriate corrective action when the flaw starts growing or propagating.

The following is the general check-list of contents to be given in the report.

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1. Date 2. Time of Day 3. Operator 4. Location-including city and state 5. Structure and its details such as material, thickness, overall dimensions etc.

6. Operating history 7. Instrumentation - manufacture and serial numbers 8. Sensor types and serial numbers 9. Calibration data - sensor and system 10. Sensor location 11. Results 12. Signatures of operator and supervisor 13. Photographs including setups and location of senors etc.

14. Hard copies of displays 15. Any other data that would be necessary to duplicate the test

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5.12 Background Noise - identification and Elimination In most applications, acoustic emission testing is always accompanied by background noises which sometimes can completely submerge the AE signal.

It is essential to understand the types of noise sources and to ensure elimination of their influence on the AE signals. A pretest noise survey, prior to AE testing, helps in knowing the frequency/magnitude of noise and deciding on the selection of sensor, filter, instrument system and test procedure. It would also help in knowing whether the AE signals can be detected above the background noise.

Frequency response of the noise helps in selection of optimum operating frequency band (sensor frequency, filtering etc.) to avoid noise.

Pretest noise survey would also identify the possible sources of noise and suggest ways to eliminate or reduce them.

Different types of noise are (1) Mechanical Noise, (2) Hydraulic Noise, (3) Cyclic noise, (4) Electrical (Electro magnetic Noise), (5) welding noise, (6) Pseudo noise (leaks and cavitations, friction in rotating equipments, growth or realignment of magnetic domains [magnetic barkhausen noise effect]) , etc.

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5.12.1 Mechanical Noise (Pseudo Noise?)

This can occur as a result of (a) Movement of mechanical parts (b) Roller bearings with spalled faces (c) Fretting noise - generally gives broad frequency spectrum (d) Vibrations due to loosely riveted, pinned or bolted structures Many a times, the mechanical noise can also be advantageously used for detection of incipient mechanical failures like in the case of bearings, turbine blades etc.

5.12.2 Hydraulic Noise 1. Leaks in hydraulic system 2. Boiling of liquid 3. Cavitation and turbulence 4. Leaking air lines

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5.12.3 Cyclic Noise This type of noise may be periodic but burst emission in nature like during a particular time of every rotation in rotating machinery, during positioning of spot welding electrodes, etc. If the noise has a low repetition rate and is uniform, it is possible to eliminate by blanking the AE recording periodically corresponding to the time period of noise generation.

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5.12.4 Electro-Magnetic Noise Electro-magnetic interference (EMI) consists of noise signals coupled to the AE instrumentation by electrical conduction or radiation. Sources of EMI include fluorescent lamps, electric motor circuits, noise from welding machines and turning on/off electric power by relays with inductive load. The electro-magnetic noise can be eliminated by providing better shielding to sensor, preamplifier and main amplifier by using high conductivity metal cases and by grounding the cases at a common point. Use of an isolation transformer on the power supply can also reduce EMI. A major problem can arise if the test structure has a ground system that is different from the AE system. The potential difference between the structure and the AE system can be substantial and may produce high frequency noise spikes much greater than the valid signals detected by the sensors. Even a sensor insulated from the structure can pick up capacitively coupled noise. A differential sensor will provide considerable relief from such noise.

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5.12.5 . Welding Noise Thermal expansion/contraction and warpage may give rise to noise from uncleaned surfaces due to cracking of mill scale etc. This can be eliminated by cleaning or descaling the plates being welded. Generally during the welding process, the component being welded is at floating potential which leads to electrical noise. In order to avoid noise due to electrically floating condition of the component being welded, it is normally recommended to remove the AE instrument ground from mains and connect it along with a blocking capacitor to the structure under test. This reduces noise but may not be legal as per safety regulation of electricity authority. It is better to use a battery operated AE system which is grounded to the surface of the structure.

Isolation transformer between the instrument and the power supply also helps in reducing the noise. It is desirable to see that all parts (sensors, premplifiers and main system) have the common ground. Arc welding gives weld spatter and fracturing of slag. One should be careful to identify and should try to eliminate these types of noise from analyzing.

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5.12.6 General Noise Elimination Methods It is possible to reduce the contribution from the noise by using filters, reducing the gain and/or increasing the threshold. However, the disadvantage is that it would affect the AE data, i.e., some of the low amplitude AE signals can not be detected. It is also possible that some of the AE signals with frequency components in the range same as noise may get filtered out. There are various methods of identifying the noise and eliminating from the processing. These include: (a) Spatial filtering and (b) Parametric filtering.

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(a) Spatial filtering When multi-channel systems are used for source detection and location, noise signals from outside the zone of coverage by the sensor array can be controlled using signal arrival time (â&#x2C6;&#x2020;T) at various sensors in the array. To accomplish this, the signal density must be relatively low (less than 1 0 per second). At higher signal density, the noise and AE signals may interact to produce erroneous â&#x2C6;&#x2020;T values. One technique for noise rejection is called the master-slave technique. In this technique, master sensors are mounted near the area of interest. These are surrounded by the guard sensors which are located relatively remote from the master sensors. If the guard sensors detect a signal before the master sensors, then the event has occurred outside the area of interest and is rejected by the instrument circuitry. Figure 5.9 shows the placement of master (data) sensors and guard sensors which can detect signals from the zone of interest (hatched portion) and reject any signals coming from outside the hatched portion.

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Fig. 5.9 Spatial filtering - master slave technique for noise discrimination and elimination (any signal outside hatched region is rejected)

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Master & Slave

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Master & Slave

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Master & Slave

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An alternative method called coincidence method is also used for noise rejection. In this method, the same signal must be recorded by all the sensors within an interval of time which is less than the time for the signal to travel from one sensor to another.

The signal is rejected if it does not satisfy this requirement. Figure 5.10 shows the sensor array arrangement and the acceptance area based on the above criterion. During fatigue testing, in order to eliminate noise from grips, spatial discrimination by using guard sensors can be adopted.

Keywords: recording of the same signal by all sensors within an interval of time which is less than the time for the signal to travel from one sensor to another.

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Fig. 5.10 Spatial filtering-coincidence method for discrimination and elimination of noise. Any signal received from outside 'accept area is rejected'

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(b) Parametric Filtering In this case, signal parameters (like Event Duration, Rise Time, Peak Amplitude, slope frequency, etc.) and stimulus are used to eliminate the noise signal from analysis. Mechanical noise has characteristics that help distinguish it from the AE signals from cracks.

Machining noise signals are relatively low frequency with slow rise time. The acoustic emission bursts from cracks generally have fast rise time. Therefore, in some cases, a rise time discriminator can be effective in isolating AE from mechanical noise. Frequency discrimination is usually a more reliable approach. Similarly, event duration, slope, peak amplitude etc. can also be used for elimination of noise.

In order to avoid crack rubbing noise from detection during fatigue testing, only the AE generated during 90% to 100% of maximum load of the fatigue cycle is recorded and analysed.

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(c) Use of Attenuation materials for noise elimination/ reduction If it is realised that there is noise source present outside the zone of interest while testing a structure/ component, it should be possible to eliminate/reduce the noise by introducing acoustic noise suppression materials between the zone of interest and noise source. For example, in the case of servo-hydraulic system used for fatigue crack growth studies, the noise from hydraulic system, like cavitation in hydraulic fluid in narrow channels of the load piston and cylinder, can be controlled by separating the servo-controller from the test frame with high pressure hose. Some of the sound attenuating materials are (1) Rubber (2) PVC (3) Teflon (4) Bakelite (5) Nylon (6) Plexiglas (7) Asbestos etc. Similar approach can be adopted wherever it is possible to isolate the noise.

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5.12.7 Pseudo Sources These are not the actual AE sources which cause transient energy release such as deformation, crack initiation and crack growth. Many times, these are unwanted when the testing is for crack detection and integrity assessment of the component/structure. However, these are sometimes useful for detection of presence of leakages, cracks (even though they are not growing), loose particles etc. The possible pseudo sources are as follows:

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1.

2.

3.

4.

5.

6.

7.

8.

Liquid and gas leakage Crack closure/rubbing Loose particles and loose parts .

Oxide and scale cracking Slag cracking Frictional rubbing Cavitation Any sudden change in volume such as (i) boiling (ii) freezing (iii) melting and (iv) chemical reactions.

9. Realignment of magnetic domain.

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The usefulness/ disadvantages of some of these pseudo sources are given below: Liquid and gas leakage Acoustic isolation between the structure to be tested and pressurization system (housing leakage) using a rubber/PVC pipe between the two reduces or eliminates the noise. Even though it is a noise during proof testing of pressure vessels, it is also a good source for actual leak detection.

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Crack closure/rubbing It is an important source for detection of cracks in the structure. However, it is a noise when we consider quantitative estimation of crack growth using AE. This can be eliminated by recording and analyzing the AE data from the portion of the load cycle where rubbing/closure are absent, say for example recording AE signals generated between 90% and 100% of the maximum load range.

Loose particles and loose parts If the aim is to detect presence of any loose particles in a system, whose presence can cause fretting/ wear/erosion damage, the noise created by them is very useful. However, presence of loose articles in a system can create problems because of the associated noise which can mask the actual AE signals from crack growth etc. Table 5.1 Summarises the unwanted noise sources and the possible elimination methods.

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Table 5.1. Unwanted noise sources and suggested methods to remove them: (Alternatives to Standard Noise Discrimination Methods)

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Questions

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Questions Q 1. Acoustic emission testing procedure specifies the following a. equipment to be used and placement of sensors b. process of applying the stimulus to the component c. data to be recorded and reported d. qualification of personnel operating the equipment and interpreting the results e. all the above Q 2. Intense sources are indicative of a. possible flaw growth b. questionable structural integrity c. no concern of the health to be structure d. none of the above

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Q 3. Background noise can be reduced by a. using flat response amplifier. b. using in-live amplifier. c. using heavier gauge coaxial cable. d. electronic filtering.

Q 4. Roller bearings with spalled 裂片状 faces are type of a. hydraulic noise b. cyclic noise c. mechanical noise d. electro - magnetic noise

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Q 5. Electro magnetic noise can be eliminated by a. providing better shielding to sensor, preamplifier and main amplifier in high conductivity metal cases and by grounding the cases at a common point. b. using a isolation transformer on the power supply. c. using a differential sensor. d. all ofthe above.

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Q 6. Coincidence method of noise elimination is based on a. recording of the same signal by all sensors within an interval of time which is less than the time for the signal to travel from one sensor to another. b. Detection of the signal by the guard sensor before the master sensor. c. Filtering the parameter like event duration, rise time, peak amplitude, etc d. Using Rubber, PVC, Telex etc.

Q 7. Pender sources (?) of acoustic emission are a. signals general due to micro cracking in materials b. signal due to decohesion and future of inclusion c. signals are not due to actual sources which cause transient energy release such as deformation, crock initiation and crack growth. d. none of the above

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Q 8. Frictional noise can be reduced by a. postponing the test. b. using bipolar (common mode) sensors c. by giving proper shielding to instruments and cables d. Tightening fasteners to eliminate rubbing and isolating for rubbing surfaces with a layer of rubber.

Electrical Noise - improper shielding and grounding Use bipolar (common mode) sensors, proper shielding of instruments and proper electrical grounding of cables.

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Q 9. In case of verification of a combination of sensors, mounting arrangement and measuring system, the difference is relative sensitivity among various channels should be less than a. 3 dB b. 5 dB c. 7 dB d. 10 dB Q 1 0. For source location techniques application, velocity of acoustic waves used is a. longitudinal wave b. plate wave c. rod wave d. Rayleigh wave (?)

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Chapter 6 Applications of Acoustic Emission Technique P. Kalyanasundaram, C.K. Mukhopadhyay, S.V Subba Rao Series Editor: Baldev Raj B. Venkatraman

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PART-A 6.1 Introduction A host of physical, chemical, mechanical and microstructural characterisation techniques are available today for characterising materials and evaluating their performance. The selection of such techniques in materials research depends on the information required and the flexibility of application.

Sometimes the use of complementary characterisation techniques are beneficial for giving comprehensive answer to problems related to materials development and also for ensuring reliable performance over the life span of the component. It is widely accepted that an interaction of probing medium with a material provides information about the material and its performance.

In this connection, the role of acoustic emission as a nondestructive testing tool for materials evaluation is widely recognised since it provides a powerful means to reveal the dynamic events thatare associated with microstructural changes in materials.

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Acoustic emission technique is widely applied for materials research and integrity monitoring of structures. Applications in the area of materials research include the studies on the deformation, fracture, fatigue, crack growth, corrosion monitoring, oxidation and phase transformation behavior of materials. In the area of structural integrity monitoring, AET is widely used for leak detection in noisy enviromnents and assessment of structural integrity of pressurised components. In this chapter, a few aspects about the plastic deformation processes in materials, leak testing and structural integrity monitoring are discussed. In the next chapter, detailed applications of AET for different materials research areas, leak testing and structural integrity monitoring are given with specific examples.

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Plastic deformation or permanent deformation, of metallic crystals occurs mainly in two ways; slip, and twinning. The degree of occurrence each of these processes is dependent largely on the characteristics ofthe particular metal.

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Fig. 6.1 Deformation by Slip

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Deformation by Slip

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Fig. 6.2 Deformation by twinning

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Deformation by twinning

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6.2 Plastic Deformation of Materials Permanent deformation of metallic crystals occurs mainly in two ways: slip, and twinning.

The degree of occurrence of each of these processes depends largely on the characteristics of the particular metal. Slip Deformation: Slip deformation is illustrated in Figure 6.1 and occurs by translation or sliding between the atomic planes within a grain. If the deformation causes more than a very minor shift, a large number of atomic planes in each grain will slide over adjacent planes to occupy new locations with new neighbours.

The planes through the crystal that are usually most subject to slip are those ofthe greatest atomic population and greatest distance between planes.

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The orientation of the planes along which slip takes place most easily will, of course, be different for different types of crystal lattices. Because of the usual random orientation of the crystals, the slip planes of many will not be in line with the direction of loading. When the best slip planes are completely out of alignment, slip may occur along other less preferred planes.

Figure 6.2 shows the type of grain deformation referred to as twinning, which seems to occur most easily under loads applied suddenly, rather than gradually. With twinning, the grain deforms by twisting or reorienting a band of adjacent lattice forms, with each unit cell remaining in contact with the same neighbors it had before deformation took place.

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6.3 Tensile Test One of the important tests for determining the mechanical properties of aterials is the tensile test. Material specimens are fastened between a fixed crosshead and a movable crosshead on a machine designed specifically for this purpose. A weighing scale is attached to the crossheads so that as they are moved apart (together for compression testing), the load imposed on the specimen can be measured. Some machine are fitted with auxiliary equipment that takes into account the loads imposed and the resulting elongation of the specimen to actually plot a stress-strain diagram of the test.

The same results can be accomplished without this special equipment by measuring the elongation as the loads are increased and plotting the individual points to develop the curve. In order that the results are reproducible and can be compared irrespective of when the tests are performed, test specimens are prepared as per the guidelines given in standards such as ASTM that dwell on fabrication of such specimens. An understanding of a tensile test can best be acquired from a stress-strain diagram made by plotting the unit tensile stress against the unit strain (elongation), as shown in Fig. 6.3. The illustration displays curve from tensile test of typical ductile steel.

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Fig. 6.3 Stress-strain diagram

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Fig. 6.3 Stress-strain diagram ultimate strength rupture strength

yield point elastic limit

work hardening

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The straight line from 'A' to 'B' indicates that the load and deformation is in the elastic range, and as long as the load at 'B' is not exceeded, the material will resume its original position and shape after removal of the load. 'B' is the elastic limit for this particular material, and loads above that limit will cause permanent deformation (plastic flow) that cannot be recovered by removal of the load. At the load represented by the point at 'C', plastic flow is occurring at such a rate that stresses are being relieved faster than they formed, and strain increased with no additional, or even with a reduction of stress. The measurement of stress at 'C' is known as the yield point. Plastic flow occurring at normal temperature is called cold working, regardless of the kind of loading system under which it is accomplished. As plastic flow takes place, the crystals and atoms of the material rearrange internally to take stronger positions resisting further change. The material becomes stronger and harder and is said to be work hardened. At the point 'D' in Fig. 6.3, the curve turns upward, indicating that the material has become stronger because of work hardening and that higher loads are required to continue deformation. The deformation rate, however, increases until at point 'E' the ultimate strength is reached.

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The ultimate tensile strength of a material is defined as being the highest strength, based on the original cross-sectional area. By this definition, ductile materials that elongate appreciably and neck down with considerable reduction of cross-sectional area, rupture at a load lower than that passed through previous to fracture. The breaking strength, or rupture strength, for this material is shown at 'F', considerably below the ultimate strength. This is typical of ductile materials, but as materials become less ductile, the ultimate strength and the breaking strength get closer and closer together until there is no detectable difference.

Many materials do not have a well-defmed or reproducible yield point. For these materials, an artificial value similar to the yield point, called yield strength, may be calculated. The yield strength is defined as the amount of stress required to produce a predetermined amount of permanent strain. A commonly used strain or deformation is 0.002 inch per inch, or 0.2% offset, which must be ¡ necessarily indicated with the yield strength value. The yield strength is the stress value indicated by the intersection point between the stress-strain curve and the offset line drawn parallel to the straight portion of the curve.

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Fig. 6.3 Stress-strain diagram

0.2% offset

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Dislocations are generated during plastic deformation of various materials.

Tensile test is one of the tests carried out for determination of mechanical properties of materials. Different parameters obtainable from tensile tests are 0.2% offset yield strength, ultimate strength and ductility.

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6.4 Acoustic Emission from Plastic Deformation in Metals and Alloys Acoustic emission during deformation of various materials has been studied in great detail. Several possible events that may dissipate energy as elastic waves during plastic deformation are: ■ ■ ■ ■ ■

activation and motion of dislocations, release of energy due to dislocation pile up leading to micro cracking, interactions of dislocations with ordered matrix, precipitates etc., unpinning of dislocations from solute atoms, annihilation, acceleration and deceleration of dislocations.

In this section, acoustic emission from motion of dislocations is discussed.

This is followed by acoustic emission during tensile deformation.

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6.4.1 Acoustic Emission from Dislocations A number of reports are available on acoustic emtsston from dislocation motions [1-5]. Acoustic emission from dislocation motion occurs when there is nearly simultaneous motion of many dislocations within a small volume of deforming material.

A number of theories on mechanism of dislocation motion have been proposed. One such theory indicates that one or a small number of dislocation break away from their pin as stress increases on a set of pinned dislocations [1]. The small stress wave produced by breakaway can serve unpinning of additional nearby dislocations and cause an avalanche of dislocation within a small volume of material during a short period of time.

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Another theory that has been proposed is that when a sample is loaded, a stress is eventually reached at which Frank-Reed (FR) or grain boundary (GB) sources are activated [2]. The newly created dislocations move along the slip plane until they are stopped by an obstacle, such as grain boundary, precipitate or other dislocations. Once activated, the source continues to produce dislocations rapidly until the back stress arriving from the pile up of new dislocations against the obstacle becomes large enough to shut off the source. The axial displacement in the sample for the process was calculated and assumed that this axial displacement is what is detected by the transducer as acoustic emission.

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In other investigations [3-5], the release of stress concentration caused by the pile up of dislocations at a grain boundary has been described as a source of acoustic emission. It was stated that initial deformation proceeds inhomogeneously with the most favorably oriented grains yielding first.

Dislocations in this yielding grains pile up at their grain boundaries and produces a stress concentration in the neighboring unyielded grains. As the stress increases, either the pile up breaks through the bonding or grain boundary sources are activated. A packet of dislocations then moves across the previously unyielded grains, releasing stress concentration caused by the pile up. A quantitative description for the dependence of AE on dislocation motion is that, as deformation proceeds, more and more obstacles to the glide of dislocation packets are produced. These obstacles such as forest dislocations and dislocation tangles progressively reduce the distance the dislocation packets move and thereby reduce the size of the elastic waves produced until most of them become undetectable. This process implies a shift in the distribution of signal amplitudes towards smaller amplitudes with increasing strain through the AE peak at the onset of plastic flow.

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Some of the possible phenomena that may dissipate energy as elastic waves during plastic deformation are:  Activation of dislocation sources  Movements of dislocations over a distance (slip area) which in turn depends on grain size, precipitates, other obstacles etc.  Release of strain energy due to cross slip  Slip in adjacent grains  Twining  Release of energy due to dislocation pile up leading to micro cracking  Interactions of dislocations with ordered matrix, precipitates etc.  Unpinning of dislocations from solute atoms  Annihilation of dislocations  Acceleration and deceleration of dislocations

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6.4.2 Characterisation of Tensile Deformation using Acoustic Emission Acoustic emission due to tensile deformation has been studied in a wide variety of materials. The results of one of the most illustrative acoustic emission experiments to date is presented in Fig. 6.4, which shows AE rate as a function of strain for a 7075-T6 aluminium tensile specimen [6].

Superimposed on the acoustic emission data is a fit of Gilman's [7] mobile dislocation plot, which gives mobile density as a function of plastic strain. The peak in the count rate at strain levels near yielding is typical of the results obtained from different metals. The excellent agreement between Gilman's equation and AE parameters is considered as a classical evidence of the close association of AE and the dislocation movement accompanying plastic deformation in metals and alloys. Using these AE results, it is easy to obtain hardening coefficient, a very important mechanical parameter. The possibility of using AET to obtain fundamental parameters such as this greatly aroused the interest of the material scientists since then and the use of this technique for materials research has increased many fold.

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Fig. 6.4 Fit of Gilman's equation for Mobile Dislocation Density and AE rate as a function of Plastic Strain [6)

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Figure 6.5 depicts the variation of cumulative count and count rate during a standard tensile test. The plots have been superimposed for better visualization and understanding. The cumulative AE count is the sum of the count of all AE events generated during the tensile test. The AE count rate is the time derivative of the AE cumulative count. It can be observed from fig.

6.5 that the beginning of the linear elastic region of the tensile curve is very quiet (i.e., low count rates and cumulative counts) or is associated with an incubation stage. The AE activity reaches its peak in the second stage right before yielding occurs. Once yielding of the material takes place, the AE activity decreases, but is still detectable until the material fails. AET is extensively used for the study of plastic deformation behavior in materials. AE generated during deformation of materials strongly depends on the microstructure, cleanliness etc.

The peak in the acoustic emission root mean square (RMS) voltage and count rate at strain levels near yielding is different for different metals and alloys, indicating that AET is capable of monitoring the differences in their dynamic behavior. This is schemiatically illustrated in Fig. 6.6.

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Fig. 6.5. AE Parameters and Stress vs. Strain during Tensile Testing.

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Fig. 6.6 Types of Acoustic Emission that occur during plastic deformation of metals [8]

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Fig. 6.6 Types of Acoustic Emission that occur during plastic deformation of metals [8]

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Fig. 6.6 Types of Acoustic Emission that occur during plastic deformation of metals [8]

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Type 1 Materials Analysis of fig. 6.6 indicates that in the case of Type I material, the higher AE activity generated during yielding is attributed to the dislocation generation, movement and formation and propagation of deformation bands during Luder's deformation. Subsequently, during smooth deformation, higher or lower emission is observed in different materials¡, depending on the microstructure. Examples of such material are carbon steel, armco iron etc.

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Type 2 Materials In some materials (FCC metals such as AI, Cu, Brass; carbon steel at higher temperatures, 523K) continuous AE with maximum amplitude at yield region occurs due to dislocation generation and movement (Type 2). Burst signals with maximum amplitude at yield region occurs in materials where twinning occurs. The examples are hexagonal and tetragonal structured materials: Zn, Sn, In, Cd, etc.

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Type 3 Materials Type 3 in Fig. 6.6 represents maximum AE at yield point and due to PortevinLe-Chatelier effect at higher strain. The Portevin-Le-Chatelier effect is due to jerky flow during deformation and is caused by solute dislocation interaction.

The maximum amplitude at yield is due to dislocation generation and movement with homogeneous deformation. The subsequent numerous peaks in the RMS voltage are due to dislocation movement with inhomogeneous deformation (formation and propagation of deformation bands). Examples are brass, Al alloys, austenitic stainless steels at higher temperatures etc.

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Type 4 Materials Type 4 in Fig. 6.6 represents AE peak at yield point and a second peak at higher strains. The first maximum is due to homogenous deformation and the second peak is due to micro crack formation. This type of AE signature is characteristic of microstructures with precipitation hardened Al alloys (2024 and 7075), cast alloy Al-Si-10Mg etc.

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Type 5 Materials Type 5 in Fig. 6.6 represents no detectable AE except at fracture. The absence of continuous type AE is attributed to energy release below threshold level during dislocation movement. Examples are high strength heat treated fine grain structural steels, cold worked materials and some austenitic steels at ambient temperatures.

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Precipitation Hardened Al Alloys

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6.5 Fatigue Test Fatigue is a phenomenon where repeated application of stress or strain results in reduction in the strength of a material. This is similar to 'fatigue' a human being experiences after prolonged work of repetitive nature. A metal may fail under sufficient cycles of repeated stress, even though the maximum stress applied is considerably less than the strength of the material determined by static test. Failure will occur at a lower stress level if the cyclic loading is reversed, alternating tension and compression, than if the cycles are repeated in the same direction time after time. Structural members subjected to vibration, fluctuation of load, or any cyclic disturbance causing deflection must be designed to have low enough stress levels so that fatigue phenomena will not cause failure. Fatigue failure normally starts at some spot where stress concentration is high because of the shape of the member or some imperfection. Holes through the material, notches or cracks on the surface, internal flaws, such as voids, cracks, or inclusions or even minor scratches and faults caused by corrosive attack on the grain boundaries, may be sources of fatigue failure. With repeated stressing, a crack starts at one of these fatigue nuclei and grows until insufficient solid metal remains to carry the load.

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6.5.1 Endurance Limit Because a material may fail under conditions of repeated loads at a stress level far below that determined by the standard strength test, a designer must know how different materials stand up under these conditions. Tests have been developed with special machines that bend plate-shaped test specimens or subject a rotating beam to a bending load for large numbers of cycles. From data collected from such tests, the endirrance limit of a material can be determined. The endurance limit is the highest completely reversed stress whose repeated application can be endured for an indefinitely large number of cycles without failure. Figure 6.7 shows a typical S-N, or endurance limit curve, where S is stress and N is number of cycles to failure.

The material represented by this curve would have an endurance limit of 42,000 pounds per square inch (290 MPa) because the curve has flattened out, and stressing at this level could be continued indefinitely without failure.

Endurance limits correlate fairly closely with tensile strength and are from about one-third to one-half the stress required to break a tensile specimen for must materials.

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Fig. 6.7 Typical S-N curve

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6.5.2 Fatigue Strength No of cycles to failure Fig. 6.7 Typical S-N curve For some materials the curve does not flatten even after several hundred million cycles. When the endurance limit cannot be determined, or it is impractical to carry on a test long enough for this determination, it is common practice to use another value, fatigue strength, to evaluate the ability of a material to resist fatigue failure.

Fatigue strength is the stress that can be applied for some arbitrary number of cycles without failure. The number of cycles for which a fatigue strength is valid must always be specified because the operating stress chosen may be at a level where the S-N curve still slopes, and indefinite cyclic operation could cause fatigue failure.

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6.6 Acoustic Emission Technique for the Assessment of Fatigue Damage in Materials Fatigue tests are usually long-term experiments. The AE sensors can detect a large amount of signals, including the noises from the load-chain during fatigue testing. Thus, signal analysis methods should be used to filter out the unwanted signals and enhance the signal to noise (SIN) ratio of the AE signals obtained during fatigue testing of materials. Typical AE signals obtained during fatigue testing is shown in Fig 6.8 [9]. AE signals during fatigue of materials can be caused by various mechanisms, such as dislocation movement, cyclic softening, crack initiation, crack closure, and ultimate failure. During initial cycles ofloading, the AE signals from the highly stressed region are related to the plastic deformation. Crack initiation is determined by the first appearance of the AE signal at low stress levels. Once the crack is initiated, the AE signals around the zero stress are thought to be caused by crack-face grinding when the cracks are closed.

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By discarding the AE signals generated due to the crack opening and closure, a plot of the cumulative AE count versus the number of fatigue cycles is made for the three stages of fatigue damage. The first stage corresponds to the first few cycles before the cyclic stress-strain curve gets stabilized. The AE signals in the first stage result from the dislocation movements and cyclic hardening or softening. The second stage, which is the crack-incubation stage, is very quiet. This stage has a steady-state dislocation motion that eventually results in the formation of microvoids and initiate microcracks. The third stage is described as an AE-active stage. In this stage, cracks start to grow and propagate through the material. Many of the AE signals in the third stage can arise from the crack-tip plastic deformation, fracture of non-metallic inclusions, microcrack coalescence, trans granular cleavage, and fracture along grain boundaries. In Fig. 6.8, 'R' is the ratio of minimum to maximum stress fatigue cycles applied.

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In low cycle fatigue, failure occurs after a relatively low number of cycles at a higher level of stress. The curve of acoustic emission total counts versus thenumber of fatigue cycles for a given stress or strain is characterized by the presence of three stages that correspond closely to the stress state in the material [10]. This is shown in Fig. 6.9 for 4340 steel [11 ]. At the beginning of the test, an initial softening (or hardening depending on the material) results in high acoustic emission activity. The initial increase in total counts is followed by a decrease in activity that accompanies macroscopic crack initiation. Propagation to failure is then observed at the third stage of fatigue.

Thus the stress amplitude versus number of cycles curve in fatigue can be represented by three stages using acoustic emission.

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Leak testing is done for locating leaks, determining the rate of leakage from one leak or from a systern and for monitoring of leakage. A leak is measured by how much leakage it will pass under a given set of conditions.Lak rate is often expressed as the product of some measure of pressure and volume, per unit of time.

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6. 7 Leak Testing In any engineering system or industrial plant, leaks are undesirable. Hence, leak testing is an important NDE activity undertaken to locate leaks. Leaks can pertain to entry of liquids or gases from pressurized or into evacuated components or systems intended to hold these liquids. Leaking fluids (common term used for liquid or gas) can penetrate from inside a component or assembly to the outside, or vice versa, as a result of a pressure differential between the two regions or as a result of permeation through a somewhat extended barrier. Leak testing encompasses procedures for one or a ombination of the following: â&#x20AC;˘ Locating (detecting and pinpointing) leaks â&#x20AC;˘ Determining the rate of leakage from one leak or from a system â&#x20AC;˘ Monitoring for leakage

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There are different terminologies used in the field of leak testing.

Some of these include, Leak: An actual through- wall discontinuity or passage through which a fluid fl~ws or permeates; a leak is simply a special type of flaw. Leakage: The fluid that has flowed through a leak. Leak rate: The amount of fluid passing through the leak per unit of time under a given set of conditions; properly expressed in units of quantity or mass per unit of time.

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Minimum detectable leak: The smallest hole or discrete passage that can be detected. Minimum detectable leak rate: The smallest detectable fluid â&#x20AC;&#x201C;flow rate A leak is measured by how much fluid leak occurs under a given set of conditions. Because leakage will vary with conditions, it is necessary to state both the leak rate and the prevailing condition to define a leak proP,erly. At a given temperature, the product of the pressure and the volume of a given quantity of gas is proportional to its mass. Therefore, leak rate is often expressed as the product of some measure of pressure and volume per unit of time- for example, torr litres per second (torr. Lis), micron litres per second (mL/s), or atmosphere cubic centimeters per second (atm cm3 Is). The two most commonly used units ofleakage rate with pressure system are standard cubic centimeters per second (stdcm3/s) and its equivalent, standard atmosphere cubic centimeters per second ( atm cm3/s ). The most frequently used unit in vacuum leak testing is torr liters per second. In the SI system, the unit is Pascal cubic meters. per second (Pa. m3/s).

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Fig. 6.8 Cumulative AE Counts vs. Number of Fatigue Cycles [9]

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Fig . 6.9 Acoustic Emission Total Count and Stress Amplitude as a function of number of Cycles for a 4340 Steel in low Fatigue [11]

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6. 7.1 Leak Testing of Pressure Systems without a Tracer Gas Leak testing methods can be classified according to the pressure and fluid (gas or liquid) in the system.

The following sections describe the common fluid-system leak testing methods. Bubble testing A simple method for leak testing small vessels is to pressurise them with a gas and then submerge them in a liquid. Leaks if any from the vessel can then be observed as bubbles escaping from the surface.

When the vessel is too large to be submerged, it can still be pressurized and bubble-forming solutions can be applied to its surface. However, care must be taken to ensure that no bubbles are formed during the process of application itself. Spraying the bubbles solution is not recommended; it should be flowed onto the surface. A sensitivity of about 10-2 atm cm3/s is possible with this method with appropriately trained personnel. Sensitivity can drop to 10-2 atm cm3/s if soap and water are used.

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Acoustic methods The turbulent flow of a pressurized gas through a leak produces sound of both sonic and ultrasonic frequencies (Fig. 6.10) [12]. If the leak is large, itcan probably be detected with the ear. This is an economical and fast method of finding gross leaks. Sonic emissions are also detected with instruments such as microphones, which have limited ability to locate as well as estimate the approximate size of a leak. Electronic transducers enhance detection sensitivity. Smaller leaks can be found with ultrasonic probes operating in the range of 35 to 40kHz, although actual emissions from leaks range to over 100 kHz. Ultrasonic detectors are considerably more sensitive than sonic detectors for detecting gas leaks. They also have the advantage of detecting the leak from a distance. The performance of an ultrasonic leak detector as a function of the detection distance, orifice diameter, and internal air pressure is shown in Fig. 6.11 [12].

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The sound level produced is an inverse function of the molecular weight of the leaking gas. Therefore, a given flow rate of a gas such as helium will produce more sound energy than the same flow rate of a heavier gas such as nitrogen, air, or carbon dioxide. If background noise is low, ultrasonic detectors can detect turbulent gas leakage of the order of 10-2 atm cm3/s.

Ultrasonic leak detectors have also been successfully used with ultrasonic sound generators when the system to be tested could not be pressurized.

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Fig. 6.10 Turbulence caused by Fluid through an Orifice [12]

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Fig. 6.11 Relation of Orifice diameter to detection distance with Ultrasonic Leak detector for various internal air pressures [12]

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6. 7.2 Leak Testing of Pressure System using Specific Gas Detectors Many available types ofleak detectors will react to either a specific gas or a group of gases that have some specific physical or chemical property in common. Leak-rate measurement techniques involving the use of tracer gases fall into two classifications: â&#x20AC;˘ Static leak testing â&#x20AC;˘ Dynamic leak testing In static leak testing, the chamber into which tracer gas leaks and accumulates is sealed and is not subjected to pumping to remove the accumulated gases. In dynamic leak testing, the chamber into which tracer gas leaks is pumped continuously or intermittently to draw the leaking tracer gas through the leak detector.

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6. 7.3 Choosing the Optimum Leak Testing Method The three major factors that determine the choice of leak testing method are: (a) The physical characteristics of the system and tracer fluid (b) The size of the anticipated leak (c) The reason for conducting the test (that is, locating or detecting a leak or measuring a leak rate)

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The physical characteristics of the system play a large role in the selection ofleak detection methods. If a tracer fluid is used, it must be ensured that it does not react with the test item or system components. In general, it is preferable that systems/components be leak tested with the actual working fluid. This eliminates errors that might result due to conversion of tracer-fluid leakage to working-fluid leakage. If tracer gas is to be used, consideration should be given to the characteristics of the gas. In most cases, gases with high diffusion rates and small molecular size, such as hydrogen and helium, are desirable. On the other hand, when probing the surface of a container filled with tracer gas, it may be desirable to use a persistent gas or a gas with a low diffusion rate. A persistent gas will remain in the leak area longer and may facilitate leak detection and location because of an increase in tracer-gas concentration. The testing method and instrument must match or be responsive to the size of the leak. The approximate working ranges of several leak detectors or leak detection methods are given in Fig. 6.12 [12]. If leakage is too large, the leak detector will be swamped. This is normally self-evident because the detector will go to full scale and stay there until it is removed from the leakage source. If the purpose of leak testing is to locate the leaks, methods that include the use of probes or portable detectors are necessary so that the surface of the test vessel can be scanned.

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Fig. 6.12 Approximate detection Ranges of various Leak Detectors or Leak Detection Methods [12]

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Leak-rate measurement techniques involving the use of tracer gases fall into two classifications:. Static Leak testing and Dynamic leak testing.

In static testing, the chamber into which tracer gas Leaks and accumulates is sealed and is not subjected pumping to remove the accumulated gases, In dynamic testing, the chamber into which tracer gas leaks is pumped continuously or intermittently to draw the leakjng tracer gas through the Leak detector. In halogen diode testing, a Leak detector is used that responds. To most gases containing chlorine,fluorine, bromine or iodine.

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Major facors determining the choice of leak testing method are: (1) physical characteristics of the system and tracer fluid (2) Size of the anticipated leak and (3) reason for conducting the test (that is locating or detecting a leak or measuring of leak rate)

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AET can be viewed as an inspection-tool. during pressure testing of pre$~Ure vessels where the presence of defects ts revealed by pressure testfng.

On-line acoustic emtssion monitoring during hydro and pneumatic testing enables the detection of growiug defects. By employing multi sensors approach it is possible to locaterthe growing defects., if any in the structure.

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6.8 AE Associated with Structural Integrity Monitoring and Leak Testing Acoustic emission technique is extensively used for structural integrity monitoring and leak testing of pressurized components. AET can be used as an inspection tool during pressure testing of pressure vessels where the presence of defects is revealed by pressure testing. Conventionally, hydro and pneumatic testing are employed for qualification and in-service assessment of industrial components operating under pressure. While these tests can indicate the presence or absence of through and through leaks, they cannot indicate the presence of harmful defects which could have grown during the testing. On-line acoustic emission monitoring during hydro and pneumatic testing enables the detection of growing defects as well as through and through leaks. By employing multi sensor approach, it is also possible to locate the growing defects, if any, in the structure. Source location techniques are also used to reject noise from insignificant sources.

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A wide variety of structures and materials (metals, nonmetals and various combinations of these) can be monitored by AET during the application of an external load. The caution here is since primary acoustic emission mechanism varies with materials, one should characterise these before applying AET to a new type of material/ structure. Once the characteristics of AE response are defined, AET can be used to evaluate the structural integrity of a component. Acoustic emission technique is also widely used for monitoring components made of fiber reinforced composite materials.

However, due to the differences in the attenuation characteristics, effective AE monitoring of fiber reinforced components requires much closer sensor spacings as compared to a metallic component of similar size and configuration. With adequate number of sensors appropriately positioned, monitoring of composite structures can prove highly effective for detecting and locating areas of fiber breakage, delaminations and other types of structural degradations.

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In structural testing, AE inspection is routinely applied on pressure vessels, storage tanks, pipelines and piping, aircraft and space vehicles, bridges, tanks, cars and a range of other industrial components. Typical applications include the detection of cracks, corrosion, weld defects, and material embrittlement. In aerospace industry AE is used to detect growing flaws that can prove detrimental in rocket motor cases during proof tests that escaped detection during radiographic and magnetic particle inspections. It has been mentioned that one of the problems faced in industrial components operating under pressure is the leakage and its early detection and location would help in taking suitable remedial measures before any catastrophe takes place.

This is particularly significant if the leaking fluid is toxic and or inflammable.

As compared to other nondestructive techniques, acoustic emission technique is extensively applied for leak testing of pressurized components.

The feasibility of leak detection by AET depends on three factors: (i) the nature of AE radiated from the leak, (ii) the attenuation between leak and sensor and (iii) the background noise. The physical origin of leak signal is the fluctuating pressure field associated with turbulence in the fluid. The actual detection of the leak depends on the flow rate as this factor decides the energy content of the leak signal.

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PART- B A general description of the application of AET for studying plastic deformation in metals and alloys, for structural integrity monitoring and forleak testing has been given in part A. In part B, a broad spectrum of applications of AET is given first and this is followed by several other applications and case studies.

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6.9 Broad Sprectrum of Application of AET ■ • • • ■ • ■ • • ■ • ■ ■ • •

Materials research Leak detection Loose part monitoring Structural integrity monitoring of components Inspection of metal bonding Weld quality control Fatigue testing of airframe members In-flight monitoring of critical structural components Integrity of composite structures Inspection of rail welds Integrity of bridges and tunnels Integrity of concrete structures Rock mechanics and behaviour analysis Slope stability Microseismic testing of stability of mines and caves

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       

Testing the strength of laminated plastics Monitoring electroplating Monitoring canned food spoilage Research in the lumber/forestry industry Inspection and testing aboard submarine Monitoring wear in moving parts and rotating components Monitoring corrosion Research related to steel, metals, and heavy manufacturing

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In part B, applications of AET for materials research such as tensile deformation and fracture, fatigue damage, fatigue crack growth, corrosion, oxidation and phase transformation will be discussed with specific examples taken from the literature. Applications of AET for on-line weld monitoring, structural integrity monitoring of components and leak detection will also be discussed with examples.

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6.10 Acoustic Emission During Tensile Deformation and Fracture Acoustic emission is extensively used for characterising deformation and fracture processes in materials. The effect of non-metallic inclusions, deformation, temperature, precipitation, etc. on the AE generated during tensile deformation studied in AISI type 316 stainless steel helped to characterise the deformation and fracture processes [13]. In this steel, a peak in ring down counts (RDC) observed during initial 2% strain range was attributed mainly to dislocation multiplication by Frank-Reed and grain boundary sources and dislocation motion during yielding [13].

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Effect of non-metallic inclusions Comparison of acoustic activity between commercial grade and nuclear grade AISI type 316 stainless.steels was made [13]. The ratio of ringdown counts (RDC) upto 2% strain to the total RDC generated upto necking reduced drastically in commercial grade steel (typically 1.2%) compared to nuclear grade steel (typically 57%). This was attributed to the presence of inclusions in the commercial grade steel causing a reduction in dislocation mean free path, which in tum reduced the AE activity generated before 2% total strain.

The logarithmic cumulative amplitude distribution for events generated in both these steels revealed generation of more number of events with higher peak amplitudes in commercial grade specimens as compared to those in the nuclear grade specimens (Fig. 6.13). All the specimens of commercial grade steel showed similar slope for the plots of peak amplitude distribution of events, indicating that the events generated in commercial grade specimens result in characteristic b-parameter (?) . Large number of events generated with lower peak amplitude in nuclear grade steel were attributed to dislocation generation and motion at the early stage of the deformation process.

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Fig. 6.13 Logarithmic Cumulative Amplitude distribution of events generated during tensile testing of AISI type 316 stainless steel

?

Non-nuclear grade,the presence of inclusions in the commercial grade steel causing a reduction in dislocation mean free path,

Microcracking of inclusion?

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Fukushima Nuclear Power Plant

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Chernobyl Nuclear Power Plant

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Qinshan Nuclear Power Plant

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Influence of carburised case Many a time, surface hardening is employed to improve wear resistance. It is necessary to ensure that a minimum ductility is available for the hardened case in order to prevent failures in service. In service, under carburising environment, carbon pick up and hardening of surface layers occurs in steels.

The hardened layers are brittle and cracking of these layers generate copious AE. It is possible to employ AET to find out the ductility of the hardened case and also to characterise the nature of AE during deformation in structures with hardened case.

Figure 6.14 shows plots of AE events vs. time for solution annealed specimen and specimen carburised in oil contaminated sodium of nuclear grade AISI type 316 stainless steel D. There is four to five times increase in the total AE events in the carburised specimen compared to solution annealed specimen.

In the solution annealed material, 40% of the total events occurred during yielding and upto 2.3% plastic deformation. The material is silent almost during the entire plastic deformation stage.

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In the solution annealed material, 40% of the total events occurred during yielding and upto 2.3% plastic deformation. The material is silent almost during the entire plastic deformation stage.

Solution Annealed Material

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On the other hand, in carburised specimen, AE was observed throughout the tensile test. In addition to normal peaks at yielding and fracture, an additional broad peak was seen extending over a strain range of 5 to 15% with a maximum at 9% strain. This peak is attributed to cracking of brittle carburised case. The observation that this broad peak initiated at a strain of about 5% indicates the ductility of carburised case to be about 5%. Also, a large number of events in the carburised specimen were of higher peak amplitudes.

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Fig. 6.14 Effect of Carburisation on Acoustic Emission during Tensile testing of AISI type 316 Stainless Steel

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Studies on plastic deformation for inference on waiting time for dislocations The predominant frequency present in the frequency spectrum of the AE signal should be inversely proportional to the duration of the dynamic event.

Thus measurements of predominant frequency of the AE signal can provide information on the time scale of the AE process and can thus be used to evaluate dynamics of source events.

The predominant frequency of the AE signal linearly increases (?) from 0.475 MHz to 0.66 MHz with increase in plastic strain from 2% to onset of necking in AISI type 316 stainless steel [13]. The shift in predominant frequency during uniform plastic deformation could be explained on the basis of dynamic events related to waiting time of dislocations at obstacles.

Attempts to correlate time-of-flight with predominant frequencies resulted in unreasonable calculated values of mean free path (a few millimeters). AE source related to waiting time of dislocations at obstacles resulted in reasonable values of mobile dislocation density (103-104 /cm2 ). For the first time, this type of information related to waiting time for dislocations could be obtained in an elegant manner [13].

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Amplification of weak acoustic emission signals In some materials, the AE generated during plastic deformation is very weak, thus posing a problem for systematic analysis and understanding the AE behaviour. Amplifications of such weak signals would ensure better characterisation of the deformation behaviour. Sometimes the signals may be below noise level and may not be amenable for time domain analysis.

As the AE signals generated in austenitic stainless steels are weak, an innovative technique has been discovered to amplify these weak AE signals [13]. This technique is based on the understanding that external injection of ultrasonic waves simultaneously during tensile deformation is expected to interact with the subcritical AE sources and give rise to enhanced AE signals which otherwise would not have got released at those stress levels.

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In Fig. 6.15, the typical time domain AE signals and the corresponding auto power spectra of continuous type AE signals for the non-injected and injected conditions are shown. The spectra of the signals with the injected condition contains a strong peak at the injected frequency in addition to peaks at new frequencies. The amplitude levels of the frequencies without injected acoustic wave are more than one order of magnitude less dominant than that with the injected wave, as may be noted from Fig. 6.16. Amplification factors of 2 to 20 could be obtained corresponding to peak-to-peak voltage of 0.2 to 0.7 V of the injected signals.

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Fig. 6.15 Time signal and its Auto power Spectrum for a typical continuous type signal

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6.11 Acoustic Emission Technique for the Assessment of Fatigue Damage AET can be used for monitoring fatigue damage in materials. An approach is given in Fig. 6. 16. In this figure, the distribution of AE events by peak amplitude during fatigue testing of an aluminium alloy has been shown. This plot shows that with increasing number of fatigue cycles, the cumulative AE events increase and the slope of the peak amplitude distribution curve decrease. A higher value of slope indicates signals having a large number of small amplitude events, whereas a lower value of slope specifies signals consisting of an increased number of high amplitude events. As the fatigue damage progresses, events with higher peak amplitude are generated and this causes the reduction in the slope of the amplitude distribution plot. Thus the change in slope would indicate the progress of fatigue damage in the material.

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cumulative AE events

Fig. 6.16 Peak Amplitude distribution of AE events during fatigue testing

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Onset of damages at fixed cycles?

6.12 Acoustic Emission for Fatigue Crack Growth Studies AET, which gives information on the dynamic changes such as plastic deformation and crack . propagation, can be applied for continuous monitoring of fatigue crack growth (FCG) in materials. During FCG, major sources of AE for a ductile material could be cyclic plasticity occurring ahead of the crack tip, whereas, for brittle materials, the crack extension at the crack tip could be the major source of AE. Apart from this, the presence of second phase precipitates, inclusions, residual stress induced microcracking in weld structure etc. can significantly influence the AE activity during FCG. AE monitoring during high-cycle FCG in 7075 Aluminum Alloy showed linear relationships between AE events and amplitudes and crack lengths [14]. The fatigue crack growth results from AE data were in very good agreement with data measured using an optical microscope.

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Fractographic observations of the crack and specimen surfaces showed that secondary cracks formed and linked to the main crack during intermediate stages of crack growth. The crack growth rate calculated from AE data reflected the formation of secondary cracks in this period. The detectability of slow growth of cracks in bridge steels has been studied by acoustic emission [15]. The extraneous events detected was attributed to having originated from the specimen grip region. At the lower crack growth rate, several thousand cycles were required to obtain one valid acoustic emission signal from crack extension. At the higher crack growth rate, approximately one or two cycles were only required for a valid AE event from crack extension. This study also showed the significance of attenuation of AE signals from a sensor located at crack tip to a sensor approximately 102 mm away from crack tip [15].

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6.12.1 Early Detection of Fatigue Crack Growth using AET In AISI type 316 stainless steel, AET has been used to show two substages in the stage II Paris regime of fatigue crack growth which could be distinguished by a change in the rate of acoustic emission activity with increase in crack growth rate [16]. The transition point in the cumulative ring down count vs. stress intensity factor (δK) plot coincides with the crack growth rate ( da/dn) vs δK plot. The AE activity increases with increase in δK during stage Ila and decreases during stage IIb (Fig. 6.17). The increase in the AE activity with increase in δK during stage IIa is attributed to the increase in the size of the cyclic plastic zone (CPZ) which is generated and developed only under plane strain conditions. The decrease in the AE activity during stage lib is attributed to the decrease in the size of the CPZ under plane stress conditions. The higher AE activity during the substage Ila is attributed to irreversible cyclic plasticity with extensive multiplication and rearrangement of dislocations taking place within the CPZ. The AE activity is found to be strongly dependent on the combination of the volume of the CPZ, average plastic strain range and the number of cycles before each crack extension. Based on this, an empirical relationship between the cumulative RDC and δK was proposed.

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Fig. 6.17 Variation in cumulative ringdown counts (n) and crack growth rate (da/dn) as a function of cyclic stress intensity range (δk) for 25 mm thick solution annealed (sa) and thermally aged (ta) specimens of AISI type 316 stainless steel

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6.13 AE During Corrosion and Oxidation Many a times, structural materials experience fatigue loading during and after exposure to air, water, or other corrosive environments. Therefore, corrosion failures usually need to be included when investigating fatigue problems.

Moreover, corrosion failures also occur under static loading, which must be considered in structural integrity monitoring applications. The fundamental aspects of AE in corrosion is the detecting and monitoring of different forms of corrosion-induced damage including active corrosion, stress corrosion cracking, hydrogen embrittlement, corrosion fatigue, and inter-granular stress corrosion cracking ¡in materials like aluminum, aluminum alloys, magnesium alloys, steels, stainless steels, and others (e.g., copper and its alloys, uranium alloys, titanium, and zirconium alloys). Various possible sources of AE signals are the initiation and growth of cracks induced by stress corrosion cracking, hydrogen embrittlement, dissolution of metal, hydrogen gas evolution, the breakdown of thick surface-oxide films, the fracture or decohesion of phases (such as precipitates, second-phase particles, and nonmetallic inclusions), plastic deformation such as slip and twinning in the vicinity of a crack and phase transforination. The mechanisms of possible AE sources are illustrated in Fig. 6.18.

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Fig. 6.18 Mechanisms of possible AE sources due to corrosion in materials

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Many investigations have been conducted in which acoustic emission has been used to detect, monitor and study corrosion and oxidation behavior in materials. The study of pitting corrosion in AISI type 316L stainless steel using AE was aimed to establish an acoustic emission system, which could detect on-going pitting corrosion and quantitatively monitor the pitting corrosion rates of various vessels or chemical equipments in service [17].

Tests were conducted at room temperature in 3% NaCl solution acidified to pH 2, at the free corrosion potential or with applied anodic polarization. AE signals were easily detected during pitting corrosion and a good correlation was observed between AE activity and pitting rate. The exact nature of AE sources within pits could not be identified clearly but the results demonstrate that acoustic emission technique can be used to detect and even monitor the occurrence of such phenomena.

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A good correlation between the AE events and calculated cumulative pit size in terms of total corroded volume of pit cells, or total area of the inner active surface of the cells was reported. During pitting and transgranular stress corrosion cracking (TGSCC) of AISI type 304 stainless steel, it was postulated that rupture of transgranular ligaments by anodic 'dissolution is the source of AE, and that TGSCC occurs by a combination of anodic dissolution and mechanical fracture [18]. AET was applied to detect oxide layer cracking during oxidation of pure titanium [19]. It was shown that during oxidation of pure titanium at 1 073K, intense AE is emitted. This was attributed to the oxide layer fracture during high temperature oxidation of titanium. It was also reported that strong AE signals were emitted during cooling of the samples.

These were correlated with the structural changes of the oxide scale.

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6.14 Acous tic Emission During Absorption and Desorption of Hydrogen in Palladium Acoustic emission technique has potential for detection of hydrogen embrittlement phenomena. Acoustic emission during absorption of hydrogen in a material occurs due to several reasons, like, crack initiation and growth induced by hydrogen, evolution of hydrogen gas bubbles during electrochemical charging of hydrogen, breakdown of oxide film present on the surface, decohesion of second phase particles, plastic deformation etc.

Acoustic emission generated during absorption and desorption of hydrogen in palladium (Pd) has been studied [20]. Pd was chosen because it is known to absorb/desorb hydrogen readily under ambient conditions. Variation of acoustic activity as a function oftime indicated different stages of hydrogen absorption during the charging process.

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The AE signals generated during charging has been attributed to the processes such as breaking of oxide film due to surface activation and formation and evolution of hydrogen bubbles during absorption. Lower event duration and lower event rise time of the AE signal corresponding to the oxide layer cracking as compared to those corresponding to the evolution of hydrogen gas bubbles has been attributed to the fact that the oxide layer cracking is relatively a more transient phenomenon than gas bubble formation and evolution process. In the discharging cycle, the desorption of hydrogen :from the specimen leads to high AE activity immediately after initiation of discharging, followed by gradual decrease in the AE activity, which reaches a minimum upon completion of desorption. This study brings out the potential of acoustic emission parameters to gain better insight into the process of charging and discharging of hydrogen in metals.

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6.15 Study of Phase Transformation using Acoustic Emission Acoustic emission technique can be used for studying phase transformation in materials. The potential of AET for on-line monitoring of a' â&#x20AC;&#x201C;martensite formation has been reported. The occurrence of martensitic phase transformation during deformation at higher strain level has been found to generate detectable emission levels [21]. The simultaneous monitoring of acoustic emission and formation of magnetic phase during tensile deformation in a Fe-Ni alloy has been used to conclude that deformationnduced martensitic transformation generates detectable emission. It has also been reported that a minimum amount of strain is necessary for the detection of AE from such transformation. AE study during tensile deformation of annealed and cold worked AISI type 304 stainless steel has shown that more is AE generated in the annealed specimen as compared to cold worked specimens at higher strain levels and this is due to higher amount of straininduced a' â&#x20AC;&#x201C;martensite formation in the annealed specimen [22].

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The increase in AE activity at low strain levels in the less cold worked (10%) specimen compared to solution annealed specimens was attributed to the easy formation of a' -martensite assisted by prior cold work. Decreased acoustic activity for higher cold worked (20% to 50%) specimens was attributed to reduced amount ofa'-martensite formation due to increased stability of the austenite after higher amount of prior cold work. A good correlation between acoustic emission and the amount of a' -martensite formed during tensile deformation in the annealed and different cold worked specimens could be established [22].

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AET has been used to study the a' -martensite formation in cold worked 304 stainless steel during cooling after ageing for 1 hour at 673K [23]. Higher amount of AE generated in the cold worked specimens as compared to annealed specimens in the temperature interval of 603K and 466K was attributed to the formation of a' -martensite in the cold worked specimens during cooling after ageing. A good correlation between acoustic activity and the amount of a' -martensite formed in different cold worked specimens was established. The temperatures corresponding to the beginning (M5) and end (Mr) of a'-martensite formation during cooling after ageing of the cold worked specimens were estimated. The ambiguity pertaining to the nature of this transfonnation as nucleation and growth or shear type, has been resolved and formation of a'-martensite has been inferred to occur by shear process [23].

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6.16 AE For On-line Weld Monitoring During the welding process, different types of defects (cracks, pores, slag inclusions, incomplete penetration etc.) can be formed in and around the welded joints. Development of online techniques for weld quality assessment would result in savings in man hours and cost. This can be realized by on-line analysis of the AE signal generated during the welding process. The principle of acoustic emission analysis is based on the fact that the formation of defects in a material is accompanied by dissipation of energy in the form of acoustic waves, which can be detected by sensors suitably laced on the surface of the material being welded. Thus AET offers the possibility of ¡discontinuities being detected as and when they occur without interfering with the operation of the welding equipment. It is also possible to use AE signals as a feed back for correcting the welding parameters. In this section, we present the typical application of AE for evaluation of resistance spot welds. AE generated during resistance spot type of welding process (RSW) consists of several stages (Fig. 4.19).

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These are: set down of the electrodes, squeeze, current flow, forging, hold time and lift-off. Different types of acoustic emission signals are known to be produced during these stages [24]. The acoustic emission signals generated can be related to the factors of weld quality. Acoustic emission occurring during set down and squeeze can often be related to conditions of the electrodes and the surface of the parts. The large, often brief, signal at current initiation can be related to the initial resistance and the cleanliness of the part. During current flow, plastic deformation and nugget expansion produce acoustic emission signals. Following the termination of welding current, some material may exhibit appreciable acoustic emission during cooling. This can be related to nugget size and inclusions. When the material in the welding zone is heated, the pressure applied by the top electrode plastically deforms the material and AE is generated. Further heating results in growth of the nugget. As soon as the current stops, the nugget begins to cool down and residual stresses occur in and around the weldment.

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Acoustic emission for on-line prediction of quality of spacer pad welds In pressurized heavy water reactors (PHWRs), Zircalloy-2 thin walled tubes are used as cladding tubes (fuel pins) to contain fuel pellets. The fuel pins are grouped into a bundle of 19 pins. Small attachments called spacer pads are used to provide necessary gap between the fuel pins in a fuel bundle. The spacer consists of two coins namely collet and pusher. These two coins are welded on to the outer diameter of fuel pins simultaneously by spot welding and this is referred to as spacer pad welding (SPW). The spacer pad welding is a resistance spot type welding process in which applied current is allowed to pass through an electrode to both the coins. The electrode is also used to apply pressure during welding. AET has been applied for on-line quality evaluation of spacer pad welding process of nuclear fuel pins and also for the assessment of shear strength of the spacer pad welds. The quality evaluation of spacer pad welds has been carried out by differentiating weld categories with the help of cluster analysis of AE signals recorded during welding. The classification of weld categories has been done by neural network analysis of the AE signals.

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Fig. 6.19 Typical Acoustic Emission response during resistance spot welding

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Cluster analysis of AE signals In this approach, AE parameter values have been mapped by subtracting the initial values from the cumulative values (CC-IC in case of counts, and CE-IE in case of energy), where CC is cumulative count, IC is initial count, CE is cumulative energy and IE is initial energy. The parameter values have also been mapped by dividing the initial values by the cumulative values (IC/CC in case of counts, and IE/CE in case of energy). Several feature-feature plots of such mapped values of the AE parameters were examined and fmally a plot, as shown in Fig. 6.20 was arrived at for the best classification of the weld categories. The mapped parameter (CE-IE) has been used as Y axis and the mapped parameter (IE/CE) has been used as X axis in Fig. 6.20. It can be noted that theY axis in Fig. 6.20 represents the acoustic energy generated during the plastic deformation and cooling stages of the welding process whereas the X axis is the mapped value of initial energy by the cumulative energy.

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It is seen from Fig. 6.20 that normal double coin welds tend to form clusters in both upper and lower regions while the single coin welds fall in the central region with (cumulative energy-initial energy) values in the range of 2000 to 20,000 for energy ratio less than 0.55 and (cumulative energy-initial energy) values coming down to 200 for energy ratio of around 0.95. The low pressure welds form cluster in the central region i.e. in the same region where defective welds fall but all the data points possess energy ratio more than 0.65. It is also seen that the defective welds comprising both single coin welds and welds made with low-pressure fall in the higher side of the energy ratio. Thus the different categories of welds namely normal double coin weld, single coin weld and welds made with low squeeze pressure tend to form clusters which re associated with different centroids.

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Fig. 6.20 Variation in Cumulative - Initial AE Energy with AE Energy Ratio

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Classification of weld categories by neural network analysis Aim of the Multilayer Perception (MLP) based approach was to classify the different types of welds in four different steps, viz. classification between: I. Nom1al double coin weld vs. abnormal weld (low electrode pressure, one coin eroded, left coin weld and right coin weld) II. Abnormal double coin weld (low electrode pressure, one coin eroded) vs. single coin weld (left coin weld and right coin weld) III. Low pressure double coin weld vs. one coin eroded weld IV. Left coin weld vs. right coin weld. The features extracted from the AE signals generated during the spacer pad welding were used as input for a MLP based Artificial Neural Network (ANN) for classification of the weld categories. The performance of the MLP was evaluated by making the selected set of features as inputs for the various classification tasks. The summary of classification results obtained is given in Table 1. In classification of normal and abnormal weld categories (step I), all the abnormal weld types were mixed to make one class and the other class consisted of dataset from normal double coin welds only.

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With an architecture of 6-20-2 i.e. with 20 nodes in the hidden layer and training done up to 10000 iterations, the final pattern error obtained was 0.029.

With this weight m~trix, a correct / classification of the test set could be obtained with a confidence of 85%. Presently the welds are evaluated on statistical basis by performing destructive shear strength tests of a few samples in a particular batch and such tests cannot be done for all the samples. Thus the 85% correct classification between normal welds and abnormal welds would be helpful to reduce the number of such destructive tests. Since the existence of two AE sources, as reflected in abnormal double coin weld, and only one source, as represented by single coin weld, separate the data points far apart in the feature space, it would be comparatively easy to make a decision boundary between these two clusters with lesser number of nodes in the hidden layer. The results obtained for step II for classification of abnormal double coin weld and single coin weld (94% overall correct classification) highlight this observation. Notably, the ANN converged below the desired pattern value of0.001 after 610 training herations only.

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The classification results obtained for step III (low squeeze pressure vs. weld with one coin eroded) and step IV (left coin weld vs. right coin weld) were lower than that in step II though the hidden layer nodes in these cases were higher (12 and 18 respectively) and training was given for almost 10,000 iterations. This could be attributed to the fact that the individual classes in the classification problems were actually subclasses, i.e. both low squeeze pressure weld and weld with one ¡coin eroded of step III belong to one class namely, abnormal double coin weld of step II, and both the left coin weld and right coin weld of step IV belong to another separate class namely, abnormal single coin weld of step II. Hence, overlapping of the clusters would occur in the feature space thus limiting the classification capability of the neural network.

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Table 6.1 Summary of results for classification of weld categories

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6.17 AET for Structural Integrity Monitoring Applications - A Few Case Studies-Hydro Testing of a Horton. Sphere Horton sphere are widely used in process industries as storage tanks for liquids or gases sometimes under large pressures. For AE monitoring during hydro testing of a Horton sphere [26], a total of twenty four sensors were used in four different groups and in four different configurations to cover the whole structure (Fig. 6.21). In group I, 12 sensors of 150kHz resonant frequency each were used in 1-5-5-1 configuration to cover the whole sphere in a triangular location mode. In group II, three sensors of same frequency were mounted in a triangular location mode to cover a specific region where an indication was observed from the ultrasonic testing carried out earlier. In group III, one broadband (1 00 kHz to 2 MHZ) sensor was mounted near the suspected region to characterise the deformation and crack growth signals enerated, if any during hydro test. In group IV, eight sensors of 150kHz resonant frequency each were placed in 1-3-3-1 configuration to cover one half of the vessel including the suspected region.

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The sensors used for Groups I, II and III were connected to the sixteen channel Spartan 2000 acoustic emission testing system. Group IV sensors were connected to eight channel Spartan AT system. The hydro test of the vessel was carried out to a pressure of 22 kg/cm2 , with periodic holds at different pressures. A reloading cycle from 20 kg/ cm2 to 22 kg/cm2 was immediately carried out following the first cycle of hydro test. During the hydro test, it was observed that acoustic emission signals were generated only during the pressure rise. With increase in pressure, AE signals were generated in the newer areas and the areas where AE occurred in the previous pressure steps, did not generate AE in the subsequent pressure steps. These signals were attributed to local micro- lastic deformation of the material. A few signals have also been generated from specific regions, particularly through out the circumference at an elevation corresponding to the concrete supports. Subsequent inspection of the vessel with the help of simulated signals indicated that the AE signals were generated from the cracks in the concrete columns which were supporting the vessel. Some of the signals could also be confirmed to be due to fracture of oxide scale or paint layer.

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Hydro-pneumatic testing of a H2S waste stripper Acoustic emission monitoring during hydro-pneumatic testing of a H2 S waste stripper with 4.25 m internal diameter of a heavy water plant was carried out (Fig. 6.22).

AE monitoring of (i) weld joint between the shell and skirt at the bottom region of the stripper, (ii) gas outlet nozzle to shell weld at top and (iii) shell to pad weld near the gas outlet nozzle was carried out.

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A total number of eight sensors of 150kHz resonant frequency each were mounted above and below the weld, between the shell and the skirt, in two rows of four sensors each thus enabling adoption of a rectangular location mode The sensors are separated by about 3.5 m in circumferential direction and 500 mm in axial direction. The shell to pad weld at top of the vessel was covered by mounting three sensors of the same frequency in a triangular location mode. All the sensors were connected to the Spartan 2000 AE system. Hydro-pneumatic test was carried out at a pressure of 26.4 kg/cm2 , with periodic holds at different pressures. The reloading cycle was immediately carried out following the hydro test from 22.5 kg/cm2 to 26.4kg/cm2 . The analysis of the AE signals generated during hydropneumatic testing indicated that there are no growing discontinuities in the weld regions monitored.

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Fig. 6.21 AE monitoring during Hydro testing of Horton Sphere Pressurization ( stage-1) Random AE activity due to 1. Mechanical rubbing between support structure and vessel 2. Paint layer peel-off Repressurisation ( stage- ) No significant AE activity AE monitoring during hydrotesting of LPG storage sphere

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AE monitoring during operation of a carbon dioxide absorber vessel of a petrochemical industry carbon dioxide absorber vessel (Fig. 6.23), in an ammonia plant of a petrochemical industry, made of low carbon steel (39 meters high and maximum diameter of 3.5 meters) had shown signs of deterioration (formation of cracks) in its conical portion during visual, liquid penetrant and magnetic particle inspections. However, it was not possible to decide whether these cracks were only in the fillet welds of cleats or penetrating into the shell thickness (dangerous from safety point ofview). Ultrasonic nonnal beam and angle beam (45 and 60 deg.) examinations were carried out after setting the equipment sensitivity levels using a full scale mock up consisting of simulated defects. Whenever defect indications were observed, the skip distances with respect to probe, beam path distance, echo amplitude etc. were recorded and evaluated for location, orientation and extent of defects.

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In order to study the crack growth and its characteristics, on-line acoustic emission testing was carried out for a continuous period of 4 months with online recording of AE data [27]. Linear location method of acoustic emission signal analysis was followed to arrive at the exact location of growing defects.

Ultrasonic testing (UT) was once again repeated after AE studies to assess the growth defects. Excellent correlation was observed between UT and AE results. Nucleation of new defects and extension of already existing cracks as identified by AE were also confirmed by UT. The results also showed that the cracks were confmed to the welds and were not entering the shell wall, thus, indicating no damage to the absorber vessel. This study also showed the capability of AET for industrial monitoring applications.

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Fig. 6.22 Acoustic Emission monitoring during Hydrotesting of Waste Stripper of Heavy Water Plant, Kota

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Fig. 6.23 Carbon dioxide absorber vessel in an Ammonia Plant of a Petrochemical Plant

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6.18 Acoustic Emission for Leak Detection Studies have been carried out to develop AET based methodologies for detection and location of leaks, even remotely with high sensitivity and reliability. The applications of AET for detection and location of leak paths in pressurised heavy water reactor (PHWR) systems [28,29] are given below.

End shield of a PHWR [28] AET was applied for detection and location of leak paths on an inaccessible side of an end shield of unit 1 of a nuclear power station in India - Rajasthan Atomic Power Station (RAPS). This methodology was based on the fact that AE signals from air and water leak have different characteristic features. Baseline data was generated from a sound end shield of a PHWR for characterising the background noise. A mock up end shield system with saw-cut leak paths was used to verify the validity of the methodology.

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Rajasthan Atomic Power Station (RAPS).

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Time domain analysis of AE signals obtained by air pressurisation of the end shield to 0.124 MPa was used. for detection and location of leak paths.

However, this analysis could not be applied for detection of subsequent leaks, found after repair and operation of the system, due to limit on maximum air pressurisation of the end shield to 0.035 MPa. Hence, frequency spectral analysis was used. Auto-power spectra showed presence of characteristic frequency associated with the air leaks (Fig. 6.24). The difference in the characteristic frequency of the signal for the two leak paths was attributed to the size, shape and morphology of the leak paths.

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Fig. 6.24 Auto power Spectra of Acoustic Emission Signals for the End Shield

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Fig. 6.24 Auto power Spectra of Acoustic Emission Signals for the End Shield

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Fig. 6.24 Auto power Spectra of Acoustic Emission Signals for the End Shield

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Pressure of a PHWR In a condition assessment campaign undertaken in another nuclear power station - unit 1 of Madras Atomic Power Station, two possible leaking pressure tubes among the 306 pressure tubes were detected using acoustic emission technique coupled with signal analysis [29]. It would not have een possible to identify the two suspect channels, but for signal analysis, as there is no ready-made methodology for this problem. Failure of regular time domain methodology, was expected, because of the poor signal to noise ratio of the leak signal. Hence advanced signal analysis approaches became essential. There was an added difficulty of carrying out the campaign with minimum man-¡ rem consumption. Plant personnel had observed heavy water leakage in the Calandria vault. Investigations revealed that the leakage is from one of the 306 pressure tubes. The only possible technique to identify the leaking pressure tube is the AET because of its potential for leak detection even in inaccessible locations by having a direct acoustic contact between the leak location and the accessible part of the component/system.

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Madras Atomic Power Station

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The high background noise associated with the leak signal, particularly due to operation of PHT pumps did not permit use of simple and routinely used time domain parametric approach. Additionally, the characteristics of the signals from different channels due to background noise are not similar and hence it was not easy to identify the leaking channel. Typical autopower spectra of AE signals for a few channels are shown in Fig. 6.25.

Therefore, the first task was to segregate the channels with similar signal characteristics into different groups and to select the group containing the suspected leaking channel. Accordingly, a group of 15 channels was short listed as the possible leaking channels among the 306 channels using the criterion that the leaking channel should have signals with frequencies above 200 kHz. In order to further narrow down the number of suspect channels, signals from these channels were obtained at two different pressure levels.

The ratio of the spectral energy between two different frequency bands, namely 700 to 1000kHz and 40 to 175kHz, and its variation with an increase in pressure were used to narrow down the number of suspect channels to two.

For both these channels, this ratio increased with an increase in the pressure.

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Fig. 6.25 Auto-power Spectra of Acoustic Emission Signals for different Coolant Channels

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Thus, AET had identified two suspect channels. Subsequent investigations by the plant personnel using vacuum testing and hydro testing confirmed that one of the channels identified by the AET has the heavy water leakage. The use of methodologies of signal processing and analysis, in this case, has helped in not only detecting the leak (in the presence of background noise) among 306 channels, but has also instilled confidence among the inspecting personnel on the reliability of their results and interpretation [29].

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6.19 AET for Geological Applications The acoustic emission phenomena can be applied to a variety of geotechnical materials. Those materials include but are not limited to soils (sandy and clayey), rocks (igneous and metamorphic), fossilized deposits (coal) and ice.

Acoustic emission monitoring can be conducted on foundations, mines, tunnels, cuts and fills, embankments, tied back and retaining walls and dams.

Acoustic emission monitoring may be useful in nearly every geotechnical application where subsurface . deformation can be anticipated. Diverse acoustic emission source mechanisms operate in geotechnical testing and the resulting stress waves are often transmitted though heterogeneous mediums. This has led to a variety of testing techniques and systems.

Frequency range of acoustic emission studies in geotechnical materials are from the low audible (25Hz) to the ultrasonic (50 kHz). The study of damage formation in jointed or bulk rock under stress is a subject of widespread interest, with relevance to both artificiaJ applications such as optimization of geothermal recovery, oil recovery, safe design of nuclear waste repositories, and rock bursts, and natural processes such as volcanism and seismology.

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For many reasons, it is important to be able to predict the time, location and intensity of potential rock fracture. Fracture development in stressed rock has been observed extensively in the laboratory by a number of methods. One approach is the direct observation of samples by microscopes. The other method involves monitoring the hypocenter distribution of acoustic emission events caused by microcracking activity. AE techniques provide an analysis of the microcracking activity inside the rock volume. AET has an important advantage over other techniques in that tests can be performed under confining pressure, which is very important in the simulation of underground conditions. Acoustic emission study during catastrophic fracture of faults in rock has been reported [30]. The time-space distribution of AE events during the catastrophic fracture of rock samples containing a pre-existing joint or potential fracture plane was obtained under triaxial compression.

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The results were discussed with respect to the prediction and characterization of catastrophic fault failure. AE activity was modeled quantitatively in terms of the seismic b-value of the magnitudefrequency relation, the self-excitation strength of the AE time series, and the fractal dimension of AE hypocenters.

The analyses revealed three long-term phases of AE activity associated with damage creation on heterogeneous faults, each clearly identifiable based on the above parameters. A long-term decreasing trend and short-term fluctuation of the b-value in the phase immediately preceding dynamic fracture were identified as characteristic features of the failure of heterogeneous faults. Based on the experimental results it was suggested that precursory anomalies related to earthquakes and other events associated with rock failure are strongly dependent on the heterogeneity of the fault or rock mass. A homogeneous fault or rock mass appears to fracture unpredictably without a consistent trend in precursory statistics, while inhomogeneous faults fracture with clear precursors related to the nature of the heterogeneity.

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Good Luck!

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