Feasibility Analysis for Implementing GPR Scanning System in Sawmills for Productivity Improvement

Feasibility Analysis for Implementing GPR Scanning System in Sawmills for Productivity Improvement Dayakar Devaru1, Nishit Banuri2, Bhaskaran Gopalakrishnan3, Udaya Halabe4, Hasan H Latif*5 Department of Industrial Management & Systems Engineering, West Virginia University, WV, USA

1,2,3,5

Department of Civil & Environmental Engineering, West Virginia University, WV, USA

4

Dayakar.devaru@gmail.com; 2nbanuri@mix.wvu.edu; 3bhaskaran.gopalakrishnan@mail.wvu.edu; 4Udaya.halabe@mail.wvu.edu, *5hhlatif@mix.wvu.edu 1

Abstract Non‐destructive scanning technologies have been applied to detect internal defects of logs to optimize log scanning. Research has been done using many non‐destructive scanning technologies with limited success for real‐time implementation. The main objective of this research is to identify the issues related to implementation of the Ground Penetrating Radar (GPR) scanning system in sawmills for purposes of yield improvement.

The paper discusses the logistics on integration of the scanning system into the existing sawmill environment. The durability of a scanning system in continuously operating sawmills and integrating the scanning system into existing sawmill operations were identified as major research aspects. The paper also evaluates the economic feasibility of implementing the GPR scanning system for three different size sawmill classes. Based on conservative estimates of revenue and initial costs, results indicate that the investment would be feasible for all the three sawmill sizes.

Keywords Sawmills, Wooden Logs, Cants, Ground Penetrating Radar (GPR), Productivity Improvement, Quality Improvement, Feasibility Study, Sawmill Economics, Lumber value yield, Scanning, Lumber Grades, Non‐Destructive Testing

Introduction Productivity is often defined as the ratio of output to input where output means the amount produced or the number of items produced and input consists of various resources employed such as labor, material, capital and services. For industries where the cost of raw material constitutes larger part of the cost of finished goods, higher productivity can be achieved through efficient use of raw material [Khanna 2004]. The process of lumber manufacturing has not changed over the years; a cylindrical log (raw material) goes in at one end of the mill and gets transformed into lumber. The sawmill industry includes facilities engaged producing dimension lumber, boards, timber, ties and woodchips from logs. The purpose of the research is to set boundary conditions for the design of the commercialized GPR technology for identifying defects in logs for improving manufacturing yield and to use sensitivity to analyze its economic feasibility. The objective of every organization is to maximize profits and to continuously improve productivity.

The typical sequence of operations for processing logs into lumber in sawmills include sorting and stacking logs, metal detection, debarking, head saw operation, re‐saw operation, edging, trimming, inspection and grading, sorting and stacking lumber, shipping. Figure 1 shows the process flow diagram of a typical sawmill.

Sawmills are becoming more similar to process industries where the production process runs continuously. This makes it vital for sawmills to utilize raw material as effectively as possible, thus achieving better volume and value yields. Figure 2 shows the breakdown of sawmill operating costs. Logs account for up to 75% of the total operating costs [US DOC 2005]. The income in sawmills is from high value lumber boards and from the low value by‐ products such as chips, saw dust and bark. Research is needed to improve the utilization of raw materials and yield from processes. This research is trying to recognize and address all the latent concerns for executing GPR scanning system in sawmills.

International Journal of Advances in Management Science, Vol. 4 No. 1, August 2015 1 2169‐7558/15/01 001‐15, © 2015 DEStech Publications, Inc. doi: 10.12783/ijams.2015.0401.01

2 DAYAKAR DEVARU, NISHIT BANURI, BHASKARAN GOPALAKRISHNAN, UDAYA HALABE, HASAN H LATIF

FIG. 1PROCESS FLOW IN A TYPICAL SAWMILL

FIG. 2BREAKDOWN OF SAWMILL OPERATING COSTS

During this research, it was noted that production rate of high grade lumber is 30% of the total lumber production at sawmills in West Virginia. In real time, sawing decisions are taken in seconds to fulfill the production demand.

It is very difficult and time‐consuming for the sawmill operator to anticipate and split the cant into better value boards. The cant is a piece of wood, produced by a canter that requires further breakdown. Canter is a machine that converts logs into a square or rectangular cross‐section or into a two‐sided cant for further processing. There has been research to enhance this critical sawmill operation by internal log scanning, and if implemented, results showed significant increase in lumber value yield.

Non‐destructive scanning technologies have been applied to detect internal log defects in order to arrive at a better log break down. Research has been done in many non‐destructive scanning technologies such as Computed Tomography (CT), X‐ray, Ultrasonic and Nuclear Magnetic Resonance. Among all these technologies, CT technology was most widely investigated scanning technology and gave better resolution of the defect areas. For automating the lumber processing in saw mills, the system should be able to collect large amount of data rapidly and process the data accurately in real time. Ground Penetrating Radar (GPR) which is widely used for civil engineering applications for Non‐destructive testing of bridges and pavements was used to scan the logs [Halabe 1995]. Though the past research in the area of GPR scanning for wood materials is limited, it has shown potential for locating subsurface defects such as knots, and decay rapidly and accurately [Agarwal 2005, Halabe 2009 and

Feasibility Analysis for Implementing GPR Scanning System in Sawmills for Productivity Improvement 3

Pyakurel 2009].

Sarigul et al. [2006] presented a paper describing the importance of detecting internal hardwood defects in order to improve the lumber grade. CT images were analyzed to locate several internal defects on different hardwood species. The paper also considers defect dependent post‐processing methods, based on mathematical morphology.

Schad et al. [1996] investigated three non‐destructive techniques such as Sound wave transmission, x‐ray computed tomography, and impulse radar for locating internal wood defects. The paper discusses the implementation of each of these techniques and difficulties in interpreting radar scanned data. All three techniques were able to detect the presence of knots but were not able to detect small areas which are of decay. Of these techniques, Computed Tomography provided highest resolution for voids, knots and high moisture contents.

Muller [2002] performed tests on timber girders of diameter varying between 0.35m to 0.40m from an existing and demolished bridge using various non‐destructive techniques such as GPR, gamma ray transmission, and ultrasound techniques. Of these techniques, GPR was found to be the most reliable method for locating internal defects. Kabir and Araman [2002] successfully conducted ultrasonic scanning experiments for detecting defects in wood pallet parts using rolling transducers. The paper describes the importance of characterization of defects for grading pallet parts. Decay has the greatest effect on the amplitude of the ultrasound signals. Bhandarkar et al.

[1998] presented a paper describing the design and implementation of computer vision based system for lumber production planning. This paper discusses about identification and classification of internal log defects from analyzing computer axial tomography images. The mathematical algorithm was capable of providing cutting pattern that improved yield and grade of resulting lumber significantly. Bhandarkar et al. [2002] described a prototype computer vision system for identification and localization of defects. The tests performed on CT images of hardwood logs from White Ash, Hard Maple, Red Oak, and Black Walnut showed 23‐63% gain in value yield recovery when compared to sawing strategy that did not use internal log defect information. In a related research, Bhandarkar et al. [2008] described the design and implementation of an automated system for planning and optimization of lumber production using Machine Vision and Computed Tomography. Optimal log sawing algorithms were designed and implemented for live sawing, grade sawing and cant sawing. The algorithms showed significant increase in value yield recovery but the execution times were not suitable for real time implementation.

Agrawal [2005] of Department of Civil and Environmental Engineering at West Virginia University successfully performed tests on wooden logs using Ground Penetrating Radar (GPR), to detect the internal defects such as rots, knots, and metals in logs. Subsurface Interface Radar (SIR)‐20 GPR system manufactured by Geophysical Survey Systems, Inc. (GSSI) and a ground coupled dipole antenna with central frequency of 900 MHz was used for data acquisition. The scanned data was processed using RADAN 5.0® software. The study was successful in detecting the defects with a depth resolution of within 4 cm and a length resolution of within 2 cm. Pyakurel [2009] of Department of Civil and Environmental Engineering at West Virginia University successfully performed tests on wooden logs and cants using GPR and explored different interpretation methods (2‐D and 3‐D imaging) to identify defects accurately. Automated Scanning System was developed to automate the movement of scanning system with respect to canted logs and study the effects of motor vibrations on GPR scanned data. Devaru [2006, 2008] of Department of Industrial and Management Systems Engineering at West Virginia University developed a MATLAB® algorithm to process the GPR scanned data. The study was successful in detecting the defects such as rot, knots and embedded metals and was also able to represent the defect location in X‐Y (distance–depth) coordinates. Jadeja [2007] of Department of Industrial and Management Systems Engineering at West Virginia University used defect detection algorithm from previous study [Devaru 2006] to develop a process plan that generated a cutting sequence for sawing cant at re‐saw machine. The generated process plan was represented as series of numbers that corresponded to the different faces of the cant. This process plan algorithm showed an increase of 13 to 21 % in lumber value yield compared to conventional sawing sequence.

Schmoldt et al. [2000] in their paper discussed about the six design and operational constraints for industrial CT scanners to become a viable reality. Schmoldt et al. discussed about important constraints such as scanning speed, scanner duty cycle, harsh environment and effective application software which needed to be satisfied before being implemented in sawmill environment. They also discussed the value gain realized from prototype testing of CT scanner at medium sized facility in Austria.

4 DAYAKAR DEVARU, NISHIT BANURI, BHASKARAN GOPALAKRISHNAN, UDAYA HALABE, HASAN H LATIF

Occena [1991] stated that the advent of non‐invasive technological developments without integrating the information involved would result in data overload for the human saw mill operator. This paper discusses about the issues related to computer integration for various technologies such as scanning, edging, trimming and grading available to hardwood sawmills. Occena described a model for computer integrated hardwood sawmill to resolve issues related to the integration of hardware and software such as scanning, edging, trimming and grading. Bowe et al. [2000] found that nearly 73% of the hardwood sawmills did not have any type of scanning and optimization technology. They stated the reason for reluctance to adopt new technology might be the low available capital in the sawmill industry. In a related research, Bowe and Smith [2006] analyzed the data collected through personal interviews with two hardwood sawmill groups including one that had adopted scanning and optimization technology and the other had not. They examined decision factors for adopting scanning and optimization technology such as production improvements, maintenance issues and barriers in hardwood sawmills. The components such as improved raw material recovery, increased production levels, increased lumber revenues, improved lumber quality and improved lumber consistency were classified into “production improvements” factor. The components such as initial cost, new mill installation and existing mill layout restriction were classified into “barriers” factor. The adopters of scanning technology rated production improvements as the most important decision factor whereas the non‐adopters rated barriers as the most important decision factor. Schmoldt [1992] presented a paper describing the importance of Computer Integrated Manufacturing (CIM) software that can exercise processing decisions with the information provided by the scanning systems. He stated that the difficult aspect to automate sawmill operations would be the natural variability and complexity of wood such as different species and defect types such as visual surface defects (e.g. knots, holes, and splits), board geometry defects (e.g. warp, crook, thickness variation),and internal defects (e.g. honeycomb splits). He also attributed the high capital investment and poor payback on investment as primary reasons for reluctance to automation improvements.

The above literature review indicates that significant effort has been devoted to developing various nondestructive technologies for use with wood based products. Non‐destructive scanning technologies such as Computed Tomography (CT), X‐ray, ultrasonic, nuclear magnetic resonance, sound wave transmission, gamma ray transmission and GPR were able to detect the defects. Among these scanning technologies CT was found to be most widely investigated technology and give better resolution of defects. The execution speeds of various data processing algorithms and cost involved were the major drawbacks for the CT scanning technology. The literature review also discusses about the concerns of hardwood sawmill industries such as high initial cost, production and maintenance issues to implement the scanning systems. Significant research has been done on using GPR scanning systems to identify the defect location and to generate cutting sequence to obtain better grade lumber. GPR scanning system also provided better resolution of defects and was found to be less expensive compared to CT scanners. There is the scope for research to evaluate the technical requirements and economic feasibility for implementing GPR scanning system in sawmills.

GPR Scanning System The laboratory setup for GPR scanning was assembled by Agrawal [2005] (Figure 3a) who utilized the Subsurface Interface Radar (SIR)‐20 system manufactured by Geophysical Survey System, Inc. (GSSI). The GPR system consisted of a mainframe unit connected to a portable laptop computer. The laptop controls the GPR data acquisition. The mainframe unit was connected to the antennas for transmitting and receiving the signals. GPR scans were acquired using survey wheel mode. The survey wheel includes an optical encoder which transmits distance information to the GPR data acquisition system using a connecting cable. The data collection parameters such as scans/second and scans/meter were user‐specified inputs that respectively affected how many scans of GPR data are collected in a second and how many scans are collected based on distance traveled. Other user defined parameters include time range (ns), samples/scan and bits/sample that affected the sample depth and resolution of the data, and the signal quality as well. The header file that contains input values must be entered by the user every time before performing the scanning process.

Ground Coupled Antennas The ground coupled antennas were used as they had the deeper penetrating ability. The antennas were mono‐

Feasibility Analysis for Implementing GPR Scanning System in Sawmills for Productivity Improvement 5

static (both transmitter and receiver on one electronic plug‐in in a single antenna housing) but could also be used in a bi‐static mode where one antenna transmitted the signal and the other one received it. Circular logs (after being debarked) were used for scanning during the research performed by Agrawal [2005] & Devaru [2006]. The unevenness of bark on logs is not suitable for ground coupled antennas because the antennas need to be as close as possible to the scanning surface for better scan. Moreover the uneven surface of the bark on logs would cause damage to the antennas but it is not feasible to replace antennas frequently as they are very expensive.

FIG. 3A GPR SCANNING SYSTEM [AGRAWAL 2005] FIG. 3B AUTOMATED SCANNING SYSTEM ASSEMBLED [PYAKUREL 2009]

FIG. 4A RADAN PROCESSED GPR DATA [AGRAWAL 2005] FIG. 4B GPR DATA PROCESSED [DEVARU 2006]

FIG. 5A DEFECT DETECTION ALGORIGHTM [DEVARU 2006] FIG. 5B PROCESS PLAN ALGORITHM [JADEJA 2007]

6 DAYAKAR DEVARU, NISHIT BANURI, BHASKARAN GOPALAKRISHNAN, UDAYA HALABE, HASAN H LATIF

FIG. 5C CANT WITH MAPPED INTERNAL DEFECTS FIG. 5D CROSS SECTION OF CANT SHOWING SAWING SEQUENCE

Automated Scanning System Setup A new scanning setup (Figure 3b) was developed by Pyakurel [2009] to automate the movement of the GPR antennas with respect to cants in order to better simulate the scanning operation in a real sawmill and study the effects of motor vibration on the acquired GPR data by Automated Scanning System. In real time scanning in sawmills, the antennas can remain stationary and the canted log can move with respect to antennas on conveyor, or the canted log can be stationary. The research performed by Pyakurel showed that there wouldn’t be any effect of motor vibrations on acquired GPR data quality.

GPR Data Processing To enhance the features in the GPR scan data, certain post processing steps such as range gain, zero correction, background removal and noise filtering were done using RADAN® software. Radar signals are prone to attenuation with increasing depth. Because of attenuation, the information at greater depths was not as clear and reliable as the data near the surface of the antenna. RADAN® software allows depth dependent gain which compensates for amplitude reduction with depth. RADAN® processed data will be in the form of an image (as shown in Figure 4a) which needs an expert to interpret the defects; and moreover it would be time‐consuming to actually implement it in sawmills. To process and analyze the data and to present the defects location in X‐Y coordinates (Figure 4b), MATLAB® algorithm was developed by Devaru [2006] in his research (Figure 5a).

Process Plan Algorithm Though the locations of defects by MATLAB® algorithm were in X‐Y coordinates, it would take time for re‐saw operator to interpret the defects and cut the cant into boards. Jadeja [2007] developed an algorithm (Figure 5b) which used the coordinate matrices generated by defect detection algorithm [Devaru 2006] to give the cutting sequence in terms of face numbers. This process plan algorithm was designed based on re‐saw operation as the canted logs were used for scanning. The defect detection algorithm was used to process the data from two antennas on adjacent faces of the canted log to obtain coordinate matrices (defects location in X‐Y coordinates). The “synthesize data module” of the process plan algorithm was developed to process the coordinate matrix data from two antennas and find the defect dimensions. The defect clusters were mapped into a 3 dimensional model and determined by subtracting each X coordinate of the matrix from its immediate predecessor and comparing the difference to a standard delta value of 3 cm (Figure 5c).Then the process plan algorithm generated the cutting sequence (Figure 5d) in terms of face numbers to get the maximum yield. This process plan algorithm showed an increase of 13 to 21 % in lumber value yield compared to conventional sawing sequence [Halabe et. al. 2011].

Need for Research Internal defects such as knots, decays and rots in hardwood logs are of major concern as they actually determine the ultimate value of the lumber. The process of selling lumber according to grades is intensifying the need for

Feasibility Analysis for Implementing GPR Scanning System in Sawmills for Productivity Improvement 7

sawmills to introduce new sawing techniques so that they will get best possible grades from the logs. Table 1 shows the average lumber prices by species, grade and thickness acquired from one of sawmills during this research.

TABLE 1 LUMBERPRICES (USD) BYGRADE ANDTHICKNESS ($/1000 BOARDFT)

Lumber Grade FAS / FAS – 1F / Selects No. 1 Common No. 2 Common FAS / FAS – 1F / Selects No. 1 Common

Cherry Hard Maple 4/4ʺ thickness boards $2,200 $2,000 $1,380 $1,020 $845 $620 6/4ʺ thickness boards $3,100 $2,950 $1,850 $1,580

Red Oak

White Oak

$1,450 $960 $630

$1,400 $800 $550

$2,400 $1,700

$2,300 $1,550

The fact that the raw material costs up to 75% of the total operating costs in sawmills makes it an economic necessity to implement some type of NDT to improve lumber value yield. Based on a survey conducted [Smith 2004] in couple of mills in West Virginia and Pennsylvania which were two major hardwood lumber producers, the production rate of high grade lumber (FAS/Selects) was less than 50%. This is due to the fact that the wood processors are not able to identify the location of defects before actually sawing. And as the steps in sawing process are irreversible, the loss involved in producing low grade lumber is also irrevocable. As a consequence many mills are operating less than optimal profits. According to various surveys conducted to determine hardwood research technology priorities [Milauskas 2005], one of the primary priorities was introduction of automated defect detection and grading systems in sawmills.

Significant research has already been performed to address the scanning, processing, and analysis of the data and generating the process plan for break‐down of cants. All this research was performed in a lab environment but the critical issue was to integrate this scanning system technology into existing sawmill environment. Concerning such as data acquisition, processing speeds, locating the scanner into existing sawmill layout and operations, and impact on production parameters have to be addressed before implementing the scanning system.

To implement the GPR scanning system in sawmills, it is important to do feasibility studies. This research focuses on determining and analyzing the potential issues for implementing GPR scanning system in sawmills. Technical issues are often seen as primary focus of a feasibility study, but it is also important to analyze whether the project is economically viable. This analysis will help to determine whether it is feasible or not to develop GPR scanning system prototype and implement it in sawmills.

Feasibility Analysis The GPR scanning system in sawmills must be able to operate up to 3 shifts, 5 days per week continuously. For bridge deck and concrete evaluations, GPR was used only for certain periods of time, but sawmills would saw hundreds and thousands of cants every day and they should be scanned in nearly a continuously manner. The environmental noise which may be from nearby power lines, interference from any electromagnetic devices or conductive materials such as conveyor system may contribute to errors in recorded scan data. Though the GPR scanning systems which are used for surveying have the ability to collect data continuously, the reliability and durability of the system in harsh sawmill environment would be a major concern. Further, the design of non‐ conductive conveyor system at the GPR scanning location would add to the cost and complexity of the scanning system installed at sawmills.

The defect detection algorithm does not specify the type of defect, but identify the type of defect is important because lumber grades depend on the type and length of the defect. Further research needs to be done to differentiate the defect types based on the signal intensities by scanning logs of various species in each of the three different log grades (FAS, Select, and Common). A better cutting sequence could be obtained by improved estimation of third dimension of the defect which would require scanning cant from four faces [Jadeja 2007]. The GPR data processing software could be developed by integrating enhanced defect detection and process plan algorithms. GPR scan data is processed by applying the following major steps to generate cutting sequence that

8 DAYAKAR DEVARU, NISHIT BANURI, BHASKARAN GOPALAKRISHNAN, UDAYA HALABE, HASAN H LATIF

yield higher value lumber. 

Converting radar data and processing individual antenna scan data by applying steps such as linear gain, zero correction, signal clipping and filtering by using the defect detection algorithm.



Detection of internal defects in individual processed antenna scan data by applying threshold value concept used by Devaru [2006]. Generating two‐dimensional coordinates for identified defects by using the defect detection algorithm.



Integrating the coordinate matrix data obtained from processing scan data of individual antennas to determine the defect dimensions. Boxing the defects using synthesize data module of process plan algorithm.



Generating sawing sequence in terms of face numbers and thickness by integrating “compare face module”, “grading module” developed by Jadeja [2007] with the cost module. A “cost module” should be used which has the cost details of the lumber based on species, grade, and dimensions. This would allow the program to generate the sequence based on the lumber value and not just only on the grade.

For real time implementation of a GPR scanning system, the processing time of algorithms would play an important role. Therefore, the algorithm should have the capability to process the radar data from multiple antennas simultaneously. This could be achieved by using Parallel Computing Toolbox™ in MATLAB® software.

The algorithm should take the data from sensors about the length, width and height of the cant to generate defect location coordinates. Further research needs to be done to verify if the data acquisition toolbox can send the data from GPR antennas directly into the algorithm. Figure 6shows the overview of the data acquisition system components.

Physical Phenomena

Sensor

Signal Conditioning

Acquisition Hardware

Data Acquisition System

Computer Drivers

MATLAB

FIG.6 OVERVIEW OF DATA ACQUISITION SYSTEM COMPONENTS

The cants produced at the head saw will be conveyed to a buffer region before entering re‐saw conveyor loop. The buffer region holds around 7 to 8 cants before they enter re‐saw conveyor system. These cants stay in the buffer region while the cants which are already on the conveyor system are processed into boards. The re‐saw operator flips the cants to the desired side every time when they arrive for re‐sawing. Therefore, the appropriate location for the GPR scanning system would be the place before the cant enters the re‐saw conveyor system (Figure 7).

After scanning and processing, the process plan (or cutting sequence) would be generated for the cant, but it may become difficult to track the cant and indicate the process plan to the re‐saw operator. It is proposed to have equipment to tag the cant with barcode on one end and mark it with face numbers on the other end at this location.

Advantages of having scanning system at this location: 

Scanning process could be completed during the time that cants wait in the buffer region and would have less impact on production output.

Feasibility Analysis for Implementing GPR Scanning System in Sawmills for Productivity Improvement 9



The scanning process could be done as if it is done at a laboratory setting keeping the cant fixed and moving the automated setup with multiple antennas (as the one developed by Pyakurel [2009]) along the cant. This would also eliminate synchronizing the scanning system or conveyor speed for GPR data acquisition process.

FIG. 7 TYPICAL RE‐SAW LAYOUT WITH APPROPRIATE LOCATION FOR GPR SCANNING SYSTEM

FIG. 8 CANT WITH FACE NUMBERS

One of the major concerns for real time implementation of GPR scanning system would be identifying the cant at the re‐saw and indicating the process plan information to the operator. So, it is necessary to design and develop a system which tags each cant with a barcode. The barcode can be generated using software such as Visual Basic, C++ and could store the cutting sequence information for the cant with generated barcode in a database. This barcode could be tagged to the cant in two ways: one is to use automated barcode printing and labeling systems and the other option is to use laser coders for printing the barcode on cant directly. A system should be designed to mark the face numbers on one end of the cant while scanning as the process plan (or cutting sequence) will be in terms of face numbers. Figure 8 shows the sample cant with face numbers. The face numbers should be marked in a way similar to the one shown in Figure 8since the cant should have at least one face number left till the last cut. If automatic cant flipping mechanism based on the process plan stored in the barcode can be developed, then re‐ sawing operation can be done easily without much interference of re‐saw operator. Otherwise, the re‐saw operator has to interfere every time to locate the correct side of the cant for re‐sawing based on process plan.

As a part of this research, time study was performed on re‐saw operation at sawmill. The findings are: 

A cant of 10 feet takes around 4 seconds for one cut.



It takes approximately 60 seconds for the cant to return back to re‐saw head when there are 7 cants in the re‐ saw conveyor loop.

10 DAYAKAR DEVARU, NISHIT BANURI, BHASKARAN GOPALAKRISHNAN, UDAYA HALABE, HASAN H LATIF



For a cant of size 8’’ x 8’’ x 10’, and board thickness of 1 inch, it would take around 512 seconds to saw 8 boards through re‐saw. Assuming the dimensions of all the 7 cants are same, it takes around 10 minutes to complete the sawing process for a cycle.

During this time, another set of 7 to 8 cants would be waiting in the buffer region to enter re‐saw conveyor loop.

The GPR scanning and data processing of 7 to 8 cants should be completed within these 10 minutes or between 75 to 85 seconds for one cant to have no impact on the production output. The total time taken by the existing SIR‐20 GPR scanning system and the execution time of the algorithms is approximately around 175 seconds for each cant.

The distribution of 175 seconds for scanning a log and processing the data as per the tasks performed are shown below.

1.

Data Entry = 15 seconds

2.

Scanning = 20 seconds

3.

Data Conversion = 50 seconds (25 seconds x 2 scanned data files)

4.

Defect Detection Algorithm = 60 seconds (30 seconds x 2 data files)

5.

Process Plan Algorithm = 30 seconds

Assuming data entry and scanning are done in main frame computer and data conversion, defect detection and process plan algorithm are run in individual personal computers, the cycle time will be around 60 seconds since the defect detection algorithm takes the maximum time out of all the tasks. Programs can be written to transfer the data from one computer to another and process it automatically. Programs can also be written to do parallel processing of last 3 tasks in the same computer, but the computer might get overloaded due to parallel data processing. While this cycle time of processing programs is going on, log scanning can be done with the log sawing time and save some time. Also, currently data conversion and defect detection algorithms are processing two files each and if this can be done in parallel, then cycle time can be further reduced. The GPR scanning speed can further be increased by using new SIR‐30 control system.

Sensitivity Analysis In this study, the feasibility of implementing GPR scanning system is evaluated for three different sawmill capacities. Sawmill capacity of 4.5 Million Board Feet (MMBF) of lumber per year was the lowest among all the re‐ saw operation. Therefore, sawmills producing lumber of 5, 10 and 20 MMBF/yr are considered for this analysis.

In this research, GPR scanning system and process plan (cutting sequence) were based on scanning cants. Hence, sawmill revenues generated through re‐saw operation is considered for analysis. For sawmills producing 5, 10 and 20 MMBF of lumber per year, approximately 3 MMBF/yr (60%), 5 MMBF/yr (50%) and 8 MMBF/yr (40%) respectively were produced through re‐saw operation. The gross annual income through lumber produced at re‐ saw can be estimated by using the following equation. p

q

r

Annual Income =    PLS  PLT  PLG  PG  TL S 1 T 1 G 1

Where, PLS = Percent of lumber species ‘S’ PLT = Percent of lumber thickness ‘T’ of species ‘S’ PLG = Percent of lumber grade ‘G’ of lumber thickness ‘T’ of species ‘S’ PG = Price of lumber grade ‘G’ of lumber thickness ‘T’ of species ‘S’ TL =Total Lumber produced at re‐saw A sensitivity analysis was performed to study the potential impact of uncertainty in the initial assumptions. The sensitivity of Net Present Value (NPV)was evaluated with respect to the changes in key factors such as initial investments, lumber value yield, revenue, operating costs and scanner downtime for different sawmill

Feasibility Analysis for Implementing GPR Scanning System in Sawmills for Productivity Improvement 11

configurations. This was done by varying values of one assumption at a time while holding all the other assumptions constant. Table 2 shows the initial assumptions made for different sawmill configurations. The important assumption of implementation of GPR scanning system in sawmill will have 0% impact on production output and will increase the lumber value yield by 10%. The initial investment for scanning system was estimated as $300,000 for 5 MMBF size sawmill using estimated values for GSSI SIR‐30 control unit with four 1,600 MHz antennas as $60,000 [GSSI 2011], installation of non‐conductive conveyor system, scanning set up, tagging and tracking system and any additional modifications as $150,000, data processing software as $50,000and $40,000 for any other additional equipment and training. For the sawmills producing 10 and 20 MMBF lumber, it was assumed that they would need additional SIR control unit and antennas as the reliability of these systems is not known in continuous operation sawmills. Hence, for these larger sized sawmills, initial cost was estimated to be $400,000.

Fixed costs include management and administrative costs, maintenance expenses and insurance costs and these costs are categorized as overhead expenses and it accounted for only 4% of total operating costs in a typical sawmill in 2005 [US DOC 2005]. For this analysis, it was estimated that overhead expenses were 10% of the installed GPR scanning equipment cost in sawmill producing 5 MMBF of lumber per year and 15% for sawmills producing more than 5 MMBF/yr. Variable costs include the labor cost for GPR scanning operator and the electrical costs for operating equipment related to the scanning process. The average hourly wage for production worker was $15.74 in 2008 [US DOL 2010]. The scanning operator is considered to be skilled labor and the hourly wage is assumed to be $30 including fringe benefits. The labor cost is estimated to be around $60,000 (8‐hr shift for scanning operator for 250 working days a year) for a sawmill producing 5 MMBF of lumber per year. The electrical costs are estimated to be no more than $1,500 per month, which translates to $18,000 per annum. The operating cost of barcode tagging and process plan marking on the cant was estimated to be $2,500. Therefore, the total operating costs are estimated to be around $110,500 for sawmill producing 5 MMBF of lumber per year. Similarly, the variable operating costs for sawmills producing more than 5 MMBF of lumber (operating more than 1 shift) are calculated and shown in Table 2.

TABLE 2 ASSUMPTIONSMADE FORDIFFERENTSAWMILLSIZES

Sawmill Size (MMBF/yr) 5 10 20

Re‐saw Output (MMBF/yr) 3 5 8

Revenue through Re‐saw ($)* 2,652,450 4,420,750 7,073,200

Initial Investment ($)* 300,000 400,000 400,000

Operating Costs ($)* 110,500 208,500 251,500

Increase in Annual Income ($/yr)* 265,245 442,075 707,320

Results and Discussion Based on the assumptions and estimations made in this analysis about lumber species and grades, lumber price, initial costs, operating costs, increase in lumber value yield and impact on production output, there seems to be a direct relationship between sawmill size (re‐saw output) and economic performance of the GPR scanning system (Figure 9). Net present worth of largest sawmill turns positive before the other sawmills as expected. Here the discounted rate is estimated as 15% to determine the present value of each annual increase. The taxes are calculated by reducing taxable income by 40% (effective tax rate).

Figure 10 shows the sensitivity of NPV results to variation in revenue. The revenue generated depends on the species, volume and the thickness of the lumber processed and the lumber grade distribution. The investment would be profitable even with 20% decrease in the revenues for all the three sawmill configurations. For sawmill producing 8 MMBF per year at re‐saw, the investment would be profitable even with decrease as high as 45% in revenues.

Figure 11 shows the sensitivity of NPV results to increase in lumber value yield. The GPR scanning system would be feasible for sawmills producing 8 MMBF at re‐saw even with value yield increase of less than 6%. In contrast, the increase in value yield should be at least 8% for sawmills producing 3 MMBF and 5 MMBF of lumber per year at re‐saw. The economic potential of the investment increases at different rates for different sawmill configurations for value yield above threshold values. Increase in cash flows would be substantially more for larger sawmills with increase in value yield. This also indicates that the decrease in value yield has the most significant negative impact

12 DAYAKAR DEVARU, NISHIT BANURI, BHASKARAN GOPALAKRISHNAN, UDAYA HALABE, HASAN H LATIF

on NPV for sawmills producing 5 MMBF or less at re‐saw.

FIG. 9 DISCOUNTED AFTER TAX CASH FLOWS

FIG.10 NPV VS VARIATION IN REVENUE

FIG. 12 NPV VS INITIAL INVESTMENT

FIG. 11 NPV VS INCREASEIN LUMBER VALUE YIELD

FIG. 13 NPV VS SCANNER DOWNTIME

FIG. 14 NPV VS INCREASE IN OPERATING COSTS

Figure 12 shows the sensitivity of NPV results to changes in initial investment. The increase in initial investment does not have significant impact on sawmill producing 8 MMBF of lumber per year at re‐saw. The implementation of GPR scanning system appears to be feasible for large sawmills even with the initial investment of $1.4 million.

The investment should be less than $450,000 and $700,000 approximately for sawmills producing 3 MMBF and 5 MMBF at re‐saw respectively to be profitable at 10% increase in value yield.

Figure 13 shows the sensitivity of NPV results to scanner downtime. The investment would be feasible even with 20% downtime (i.e., 50 operating days) for sawmills with re‐saw output of 3 and 5 MMBF per year. For sawmill producing 8 MMBF per year at re‐saw, the investment would be feasible even with downtime as high as 40% (100 operating days).

Figure 14 shows the sensitivity of NPV results to increase in operating costs. Even increase of130% operating costs does not have significant impact for sawmill processing 8 MMBF at re‐saw. The operating costs should be less than $150,000 for 3 MMBF and $300,000 for 5 MMBF of lumber processed at re‐saw for the investment to be feasible.

Feasibility Analysis for Implementing GPR Scanning System in Sawmills for Productivity Improvement 13

Among all the factors, decrease in lumber value yield has significant impact on the feasibility of the implementation of GPR scanning system in sawmills. The initial investment also affects the economic performance of the scanning system for sawmills producing 3 MMBF and 5 MMBF of lumber per year at re‐saw. The results indicate that sawmills produce 8 MMBF or more at re‐saw would have least amount of risk and highest possible return on investment (ROI) from installation of GPR scanning system.

Conclusions This study investigated the feasibility of implementing GPR scanning system in sawmills. The technical requirements for GPR scanning system and the data processing software were analyzed. The durability of scanning system in continuously operating sawmills, and integrating scanning system into existing sawmill operations were identified as major technical concerns for implementing GPR scanning system. GSSI SIR‐30 control unit has the ability to collect radar data from four channels at higher speeds compared to SIR‐20 control unit. The scan data from four antennas would enhance the internal defect information but processing time of the algorithms might increase. The introduction of quad‐core processors which has the 8‐way multi‐tasking capabilities and the parallel computing techniques in MATLAB® software would help in processing the data from four antennas simultaneously which would significantly reduce the execution times of processing algorithms.

The location for GPR scanning setup has been identified to be the place just before the cants entering re‐saw conveyor loop. A method to track the cants after scanning and indicate the process plan to re‐saw operator has been proposed. The automated flipping of cant without the intervention of re‐saw operator has been investigated, but it is not possible with the current barcode technologies and the major concern would be cants following one another in quick succession at re‐saw. Time study with the proposed scanning location indicated that the scanning and data processing time should be kept to approximately less than 75 seconds. The log scanning time and the processing times of algorithms developed by Devaru [2006] and Jadeja [2007] were less than 75 seconds and hence GPR scanning could be easily integrated into sawmill operation to improve lumber value yield. But identifying defect types (i.e., rot, knot) would be crucial to obtain a better cutting sequence as these defects effect the grades and value of the lumber produced.

This study also investigated the economic feasibility of implementing GPR scanning system for three different size based sawmill configurations. Based on the conservative estimates of revenues and costs, the GPR scanning system seems feasible for all the sawmill configurations. Sensitivity analysis indicates that the decrease in lumber value yield has the greatest impact on economic potential. The initial cost is also a major factor for small and medium sized sawmills. Scanning cants using GPR technology appears to be promising and warrants further research to make it a viable reality.

Future work for this research would be to actually simulate the GPR scanning operation under sawmills conditions such as continuous data collection on cants to test its durability. Further research needs to be done on barcode readers to integrate it with flipping mechanism and re‐saw operation. Research should be done to test the signal quality at different sampling rate and at higher data acquisition speeds using SIR‐30 control unit. Scanning should be performed on cants of different species and grades to know how it increases in value yield vary by species and log grades.

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Feasibility Analysis for Implementing GPR Scanning System in Sawmills for Productivity Improvement 15

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DayakarDevaruis a Ph.D candidate at Industrial and Management Systems Engineering department ofWest Virginia University. He received his Bachelor’s in Mechanical Engineering from University of Mysore in 1998 and Master’s in Industrial Engineering from West Virginia University in 2006. He is a ‘Certified Energy Manager’ from ‘Association of Energy Engineers’ and a qualified specialist of ‘Process Heating Assessment and Survey Tool’ from US Department of Energy. He has received ‘Outstanding Industrial Assessment Center Engineering Student’ award for the year 2014 from US Department of Energy. He worked on developing an Algorithm to detect defects in wooden logs using ground penetrating radar for his Master’s thesis and currently working on Predicting Energy Intensity in Wood Processing Sawmills based on Analysis of Product, Process and System parameters for his Ph.D dissertation. He is also an ‘Engineering Intern’ from ‘West Virginia Board of Registration for Professional Engineers’.

NishitBanuri is working as a project engineer at Casco, USA. He was a graduate student from the Department of Industrial and Management Systems Engineering (IMSE) at West Virginia University (WVU). During his Master’s program, he was also a part of the research assistant team at WVU’s Industrial Assessment Center (IAC) funded by the US DOE. Mr. Banuri obtained his M.S. degree in Industrial Engineering from the IMSE department at West Virginia University.

BhaskaranGopalakrishnan is a Professor of Industrial and Management Systems Engineering (IMSE) at West Virginia University (WVU) and Director of the Industrial Assessment Center (IAC) funded by the US DOE. Through the IAC and as a private consultant, he has conducted numerous industrial and energy assessments as well as plant wide energy assessments for manufacturing facilities and has published widely in this field. Dr. Gopalakrishnan obtained his B.E. (Hons) degree in Production Engineering from the College of Engineering at Guindy, University of Madras, India, M.S. degree in Operations Research from Southern Methodist University, and Ph.D degree in Industrial Engineering and Operations Research from Virginia Tech. He is a Certified Energy Manager (CEM) certified by the Association of Energy Engineers, Atlanta, Certified Practitioner in Energy Management Systems (CPEnMS), and a US DOE Qualified AirMaster+ Specialist, SSAT Qualified Specialist, FSAT Qualified Specialist, PSAT Qualified Specialist, and a PHAST Qualified Specialist. He is a registered Professional Engineer (PE) in the State of West Virginia. His areas of research interest are industrial energy conservation, waste reduction, and productivity improvement.

Udaya B. Halabe is a Professor of Civil and Environmental Engineering at West Virginia University, Morgantown, WV. He received his B.E. degree in Civil Engineering from University of Roorkee, India in 1984, and M. Tech. degree in Civil/Structural Engineering from the Indian Institute of Technology (IIT) Kanpur, India in 1985. He received M.S. and Ph.D. degrees in Civil/Structural Engineering from the Massachusetts Institute of Technology (MIT) in 1988 and 1990, respectively. He also received M.S. degree in Management from MIT in 1990. He is a Fellow of the American Society of Civil Engineers (ASCE), the Structural Engineering Institute (SEI) and the American Society for Nondestructive Testing (ASNT), and member of the American Concrete Institute (ACI) and the American Society for Engineering Education (ASEE). He has received the Statler College Outstanding Teacher Award for 2009‐2010 and numerous teaching awards from the CEE Department. In 2012, he received the James M. Robbins National Excellence in Teaching Award presented by Chi Epsilon, the National Civil Engineering Honor Society. He is a member of two honorary societies, Chi Epsilon and Tau Beta Pi. Dr. Halabe has over twenty‐five years of research and field testing experience in the area of nondestructive testing and evaluation of civil infrastructure. He has published over 100 technical papers and over 40 research reports in the area of nondestructive evaluation of timber, steel, concrete, and composite structural components. His expertise includes the use of several nondestructive testing techniques such as ground penetrating radar, infrared thermography, ultrasonics, and vibration based technologies.

Hasan H. Latifis a graduate student in Industrial and Management Systems Engineering Department (IMSE) of West Virginia University (WVU). He is playing a significant role as a research assistant in the Industrial Assessment Center (IAC) funded by US DOE. His research interest includes energy efficiency, sustainable manufacturing, and operations management. He achieved BSc. in Industrial and Production Engineering (IPE) from Bangladesh University of Engineering and Technology (BUET) where he served as a lecturer also. He is a certified six sigma green belt holder from American Society of Quality (ASQ). He is affiliated with Institute of Industrial Engineers (IIE), Society of Manufacturing Engineer (SME), Alpha Pi Mu, Project Management Institute (PMI), and American Society of Quality (ASQ).