A Statistical study on effects of fundamental machining parameters on surface topography

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A Statistical study on effects of fundamental machining parameters on surface topography Manikandan H 1 (Author), Saurabh Jagtap2 (Author), Tufan Chandra Bera3 (Author) Dept.of Mechanical Engineering,Birla Institute of Technology and Science,Pilani,Rajasthan,India 1 Birla Institute of Technology and Science,Pilani,Rajasthan,India 2 Birla Institute of Technology and Science,Pilani,Rajasthan,India 3 Birla Institute of Technology and Science,Pilani,Rajasthan,India Pilani,India 1 manikandan.h@pilani.bits-pilani.ac.in

Abstract: Roughness consists of the irregularities of the surface texture, usually including those irregularities that result from the actions involved in the production process. Surface roughness is an important measure of the quality of a machined product and a factor that greatly determines manufacturing cost. In this work,in order to estimate surface quality and dimensional precision properties in advance, theoretical models are employed making it feasible to do prediction in function of operation conditions and machining parameters such as feed speed and depth of cut etc. The need for statistical method like DOE for studying the relationship between the machining parameters is because of this need for prediction. It is a analysis technique which uses the regression method to find out the relationship between various factors in a DOE setup depending upon the interactions of the predictor variables and the response variables which is performed in the experiments. The research in this domain will help advance further investigations into the relationship between the machining factors and the surface quality of the machined components. The DOE using Taguchi’s method and statistical study of the experimental data helps to understand the interaction between various factors like speed, feed and depth of cut in the machining.

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Keywords: Surface Roughness, Taguchi Method, Orthogonal Arrays, Design of experiments, Response Surface Method

1. INTRODUCTION The surface finish of machined components has considerable impact on some properties such as wear and fatigue strength. Thus, the quality of the surface is truly important in the evaluation of the productivity of machine tools and mechanisms of production, and mechanical components. Fixing a proper cutting condition is really important regarding this because these determine surface quality of manufactured components. In order to know surface quality and dimensional precision properties in advance, it is always better to employ mathematical models making it easier to do prediction in function of operation conditions.The mechanism for the finalisation of surface roughness is very dynamic, complicated, and process dependent so that it is very difficult to calculate its value through theoretical or calculation based analysis.This presents the need for statistical method like DOE for studying the relationship between the machining parameters and roughness of surfaces.

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International e-Journal For Education And Research-2017 Nomenclature Ra Roughness average of the measured surface. h Height of the wall specimen used t Thickness of the wall specimen used l Length of the wall specimen used DOE Design of Experiments RSM Response Surface Method

Roughness consists of the irregularities of the surface texture, usually including those that result from the actions included in the manufacturing process. There are number of parameters that can be used to define the surface roughness but we choose Roughness Average.

Figure 1. Parameters in Surface Roughness

Baradie [3] developed a response model (surface roughness) utilizing factorial design of experiment and response surface methodology. They used 2^3 factorial design with a centre composite design- 12 experiments altogether.Yusuf Sahin and A. Riza Motorcu [2]used Taguchi method with L18 orthogonal array 3 factors and 5 level CCD First-order and second-order model predicting equations for surface roughness have been established by using the experimental data.

Julie Z. Zhang , Joseph C. Chenb, and E. Daniel Kirby [4]used Taguchi method using L9 array and ANOVA analysis.They were using Taguchi design application to optimize surface quality in a CNC face milling operation.M.Y. Noordin, V.C. Venkatesh , S. Sharif , S. Elting and A. Abdullah worked with DOE[5] using RSM in 2^3 factorial design to establish a 2nd order model using Least square method and ANOVA method.

The research in this domain will help advance further investigations into the relationship between the machining parameters and the surface quality of the machined components. The DOE using Taguchi’s method and statistical analysis of the experimental data helps to understand the interaction between various factors in the machining.

1.2 Roughness average (Ra)

1.1 Literature Review Many researchers have investigated the impact of machining parameters on the surface quality of various materials by using numerous methods of DOE (design of experiments) to find out the empirical relationship between various factors affecting the surface quality of a machined surface.

Süleyman Nes eli , Süleyman Yaldız and Erol Türkes determined RSM method [1] to estimate the surface roughness in turning of mild steel by making use of Taguchi L27 orthogonal array.

Mohamed A. Dabnun, M.S.J. Hashmi and M.A. ElIDL - International Digital Library

This parameter is also known as the arithmetic mean roughness value, AA (arithmetic average) or CLA (center line average). Ra is universally recognized and the most used international parameter of roughness.

Where Ra = the arithmetic average deviation from the mean line L = the sampling length

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International e-Journal For Education And Research-2017 1.3 Factors affecting surface roughness

2.3 Taguchi Method using Orthogonal Array

The depth of cut ,the feed rate per cutter ,the cutting ,the engagement of the cutting tool (ratio of cutting width to cutting tool diameter,the cutting tool ,the use of cutting and the three components of the cutting force.

Orthogonal arrays are special standard experimental design which requires only a small number of experimental trials to find the main effects of factors on output. It is a highly fractional orthogonal design that is based on a design proposed

by Genichi Taguchi and allows you to consider a selected subset of combinations of multiple factors at multiple levels. Taguchi Orthogonal arrays are balanced to ensure that all levels of all factors are considered optimally.

Planning phase: State the problem.

State the objectives of the experiment.

Select the quality characteristics and the measurement systems.

Select the factors that may influence the quality characteristics.

Select levels for the factors.

Select the appropriate Taguchi fractional matrices or orthogonal arrays (OAs).

Select the interactions that may influence the response variable.

Assign factors to OAs and locating interactions.

Select and mention the noise factors.

Execution phase: Conduct the experiment as described by the OAs. Analysis phase: Analyse the experimental results, e.g. using analysis of variance (ANOVA), response surface method (RSM).

Conduct a confirmation experiment.

2. METHODOLOGY 2.1 DOE- Design of Experiments The response surface methodology (RSM) and Taguchi techniques for design of experiments (DoE) are most wide-spread techniques for the prediction of surface roughness.

2.2 Full Factorial Method When there are two or more factors each at two or more levels, a treatment is defined as a combination of the levels of each factor. In a factorial experiment, all possible combination of the levels of each factors are represented for each complete experimentation. The number of experiments is equal to the product of the number of factor levels and can therefore become very big when either the factors are more or the levels are numerous.

Method includes Planning phase: State the problem.

State the objectives of the experiment.

Select the factors that may influence the quality characteristics.

Select levels for the factors.

Determine the number of experiments to be carried out.

Execution phase: Conduct the experiment as described by the full factorial design.

Analysis phase: Analyse the experimental results, e.g. using analysis of variance (ANOVA), response surface method (RSM).

Conduct a confirmation experiment.

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2.4 RSM – Response Surface Methods RSM is a statistical technique based on multiple regressions. With RSM, the effect of two or more parameters on quality criteria can be calculated and real values are obtained in RSM. In this way, the values that are not actually tested using experimental sets of values themselves can be estimated and the combinations help in doing this. The results can expressed in 3D series or counter map.It is a analysis technique which uses the regression method to find out the relationship between various factors in a DOE setup.

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International e-Journal For Education And Research-2017 2.5 DOE using Minitab 16

Symbol

Factor

Unit

r

Nose Radius

mm

k

Approach Angle

Degree( 0 )

y

Rake Angle

Degree( 0 )

Minitab 16 is a useful tool for design and analysis of experiments. It is possible to setup a DOE if one has decided the number of factors and there corresponding levels. Following flowchart represents the method for setup of DOE in Minitab 16.

2.6 Flowchart

Symbol r k y

Level 1 0.4 60 -9

Level 2 0.8 75 -6

Level 3 1.2 90 -3

3.2 Experiments Step 1 Select the Stats DOE Taguchi Create Taguchi design Figure 2. Selecting the levels in minitab

3. EXPERIMENTAL DETAILS 3.1 The sample data The sample data is used (for check only) from [1] where it presents the DOE analysis using RSM for establishing the empirical relationship between the machining factors like Nose radius (r), Approach angle (k) and Rake angle (y).

Table 1. Independent Variables and Levels for Model Body

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Step 2 Select the Levels factors select the design L27 enter the values of factors Figure 3. Entering the values of factors3

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International e-Journal For Education And Research-2017

Step 3 Following experimental data is selected Table 2. Experimental Data

r

k

y

Ra

0.4

60

-9

2.025

0.4

60

-6

2.283

0.4

60

-3

2.892

0.4

75

-9

2.358

0.4

75

-6

2.85

0.8

90

-9

5.01

0.8

90

-6

7.944

0.8

90

-3

7.99

1.2

60

-9

4.475

1.2

60

-6

5.065

1.2

60

-3

5.967

1.2

75

-9

4.796

1.2

75

-6

7.662

1.2

75

-3

8

1.2

90

-9

5.874

1.2

90

-6

8.665

1.2

90

-3

8.951

Step 4 Select

the

Stats DOE

Response

Surface

Method Define Custom Response Surface design 0.4

75

-3

3.962

0.4

90

-9

3.509

0.4

90

-6

4.099

0.4

90

-3

4.876

0.8

60

-9

4.225

0.8

60

-6

5.142

0.8

60

-3

5.692

0.8

75

-9

4.308

0.8

75

-6

6.066

0.8

75

-3

6.563

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select the factors select the response variable ok.

Figure 4. Selecting the response variable

4.Design,Modelling and Manufacturing of Experimental specimen.

With the literature review and the study of the DOE models it was planned to carry out the experimentation

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International e-Journal For Education And Research-2017 to determine the effects of the following machining parameters.

Radial depth of cut Axial depth of cut Feed per tooth Each factor to have 3 levels. Taguchi model for DOE is selected with L9 orthogonal array.

The experimental model is designed to acquire data for the statistical analysis and establish the relationship between the machining parameters.

The thin wall model is considered with aluminium as the material parameters of design as defined below For thin wall consideration

Figure 5(c). Specimen

Wall height in mm Wall thickness in mm Wall length in mm With the available material block we have wall length of 75 mm, wall thickness of 5mm and height as 40 mm as per the above mentioned formula.The detailed design of the block is mentioned below with dimensions (all dimensions in mm) Figure 5(a) Drawing of the part

Figure 5(d).Experimental Setup( including Renishaw touch trigger Probe)

Figure 5(b). Model

4. RESULTS AND DISCUSSION IDL - International Digital Library

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International e-Journal For Education And Research-2017 Step 6

To get the graphs of main effect

Results of the RSM analysis are obtained as seen

Select the Stats DOE Taguchi design

Taguchi Define Custom

select the factors select the

response variable ok.

Select the Stats DOE Taguchi design

Taguchi

Analyse the

select the factors select the

response variable ok.

Figure 6. Results

Figure 8. Plots

Figure 7. Results

Step 8 To get the graphs of Interaction effects Select the Stats ANOVA

Interaction Plots

select the factors select the response variable select full interaction matrix

ok.

Step 9 To get the Response Surface plots

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International e-Journal For Education And Research-2017 Select the Graphs 3D surface plot

select the

factors select the response variable ok.

Figure 9. Ra Plots

5.CONCLUSION

Figure 10. Ra Plots

Tasks were completed during experimentation and analysis.Initially a study about the design of experiments and application of same was conducted.

Then selection of DOE method depending on the number of factors and levels were carried out, afterwhich the design of workpiece for experimentation was done.MINITAB analysis of data generated from sample data set was performed and the relationship between the machining parameters and roughness was established. So predicting optimal value of surface roughness for given set of machining parameters became possible.

6.ACKNOWLEDGEMENTS The author thank and acknowledge the Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India for financial support to carry out this research work (Project No: SERB/ETA-0003/2013).

7.REFERENCES

Figure 11. Ra Plots

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[1] Neşeli, S., Yaldız, S., & Türkeş, E. (2011).

Optimization of tool geometry parameters for turning operations based on the response surfac methodology.Measurement, 44(3), 580-587.

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International e-Journal For Education And Research-2017 [2] Sahin, Y., & Motorcu, A. R. (2005). Surface roughness model for machining mild steel with coated carbide tool. Materials & design, 26(4), 321-326.

[3] Dabnun, M. A., Hashmi, M. S. J., & El-Baradie, M. A. (2005). Surface roughness prediction model by design of experiments for turning machinable glass– ceramic (Macor). Journal of Materials Processing Technology, 164, 1289-1293.

[4] Zhang, J. Z., Chen, J. C., & Kirby, E. D. (2007).

Surface roughness optimization in an end-milling operation using the Taguchi Arbizu, I. P., & Perez, C. L. (2003). Surface roughness prediction by factorial design of experiments in turning processes. Journal of Materials Processing Technology, 143, 390-396.

[5] Noordin, M. Y., Venkatesh, V. C., Sharif, S., Elting, S., & Abdullah, A. (2004). Application of response surface methodology in describing the performance of coated carbide tools when turning AISI 1045 steel. Journal of Materials Processing Technology, 145(1), 46-58.

[6] Amitava Mitra ,Fundamentals of Quality Control and Improvement 3rd edition. Wiley publication.

[7] Mohanta D K(2012).Prediction of Surface roughness in turning of low carbon steel with coated and uncoated inserts.International journalof advanced materials manufacturing and characterization March 2012 Vol 1 Issue 1 [8] Vikas,Apurba Kumar Roy,Kaushik Kumar(2014).Effect and Optimization of various machine process parameters on the surface roughness in EDM for an EN41 material using GreyTaguchi.Third International Conference on Materials Processing and Characterization(ICMPC 2014),385390

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