Discriminant analysis-based parametric study of an electrical discharge machining process

Document Type : Article

Authors

Department of Production Engineering, Jadavpur University, Kolkata, West Bengal, India

Abstract

This paper deals with the application of discriminant analysis in an electrical discharge machining (EDM) process to determine the comparative contribution of each of its input parameters on the measured responses. It also identifies the most significant EDM process parameters influencing those responses. For this process, voltage, current, pulse-on time and pulse-off time are considered as the input parameters, whereas, material removal rate, electrode wear rate and surface roughness are the responses. Based on the past and simulated experimental data, both simultaneous and step-wise estimations are carried out for each of the three responses showing the relationships between the EDM process parameters and the considered responses. It is observed that in both these estimations, pulse-off time, current and pulse-on time respectively evolve out as the most significant parameters for material removal rate, electrode wear rate and surface roughness. Step-wise estimation identifies voltage as the least significant input parameter for all these responses. The developed discriminant functions, which can also help in predicting the responses, are finally cross-validated.

Keywords


References:
1. Rajesha, S., Sharma, A.K., and Kumar, P. "On electro discharge machining of Inconel 718 with hollow tool", Journal of Materials Engineering and Performance, 21(6), pp. 882-889 (2012).
2. Jameson, E.C., Electrical Discharge Machining, Society of Manufacturing Engineers, Dearborn, Michigan (2001).
3. Schubert, A., Zeidler, H., Hackert-Oschatzchen, M., et al. "Enhancing micro-EDM using ultrasonic vibrations and approaches for machining of nonconducting ceramics", Strojniski Vestnik - Journal of Mechanical Engineering, 53(3), pp. 156-164 (2012).
4. Lin, Z., Liu, Y., and Zhang, L. "Research of EDM (electrical discharge machining) process simulation based on grey neural network", In: D. Jin and S. Lin, (Eds.) Advances in Mechanical and Electronic Engineering, Springer, Heidelberg, 177, pp. 373-379 (2012).
5. Debnath, S., Rai, R.N., and Sastry, G.R.K. "A study of multiple regression analysis on die sinking EDM machining of ex-situ developed Al-4.5Cu-Sic composite", Materials Today: Proceedings, 5(2), pp. 5195- 5201 (2018).
6. Singh, R., Debnath, R., and Rai, R.N. "Study the effect of machining process parameters on die-sinking EDM for Al5083/B4C composite", Journal of Scientific and Industrial Research, 78, pp. 615-619 (2019).
7. Gudipudi, S., Nagamuthu, S., Subbian, K.S., et al. "Fabrication and experimental study to optimize the recast layer and the material removal in electric discharge machining (EDM) of AA6061-B4C composite", Materials Today: Proceedings, 19(2), pp. 448-454 (2019).
8. Kishan, B., Sudheer Premkumar, B., Gajanana, S., et al. "Development of regression model for Al6061 + SiC tool and optimization of process parameters on electro discharge machining", IOP Conference Series: Journal of Physics, 1455, pp. 1-10 (2020).
9. Kumar, S., Dhingra A.K., and Kumar, S. "Parametric optimization of powder mixed electrical discharge machining for nickel based superalloy inconel- 800 using response surface methodology", Mechanics of Advanced Materials and Modern Processes, 3(7), pp. 1-17 (2017).
10. Sinha, S., Ballav, R., and Kumar, A. "Investigation of material removal rate and tool wear rate on electrical discharge machining of Incoloy 800HT by using response surface methodology", Materials Today: Proceedings, 4, pp. 10603-10606 (2017).
11. Rajneesh, R., Subhash, S., Mulik, R.S., et al. "Study of machining performance in EDM through response surface methodology", In: K. Shanker, R. Shankar and R. Sindhwani, (Eds.) Advances in Industrial and Production Engineering, Springer, pp. 207-216 (2019).
12. Soundhar, A., Zubar, H.A., Sultan, M.T.B.H.H., et al. "Dataset on optimization of EDM machining parameters by using central composite design", Data in Brief, 23, Article 103671, pp. 1-11 (2019).
13. Aich, U. and Banerjee, S. "Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization", Applied Mathematical Modelling, 38(11-12), pp. 2800- 2818 (2014).
14. Jiang, H., Lin, Y., Wang, S., et al. "Research on optimum processing parameter of EDM based on support vector machine", Applied Mechanics and Materials, 775, pp. 229-233 (2015).
15. Rajesh, R. and Anand, M.D. "Prediction of EDM process parameters for AISI 1020 steel using RSM, GRA and ANN", International Journal of Recent Technology and Engineering, 8, pp. 51-63 (2019).
16. Bharti, P.S. "Process modelling of electric discharge machining by back propagation and radial basis function neural network", Journal of Information and Optimization Sciences, 40(2), pp. 263-278 (2019).
17. Ong, P., Chong, C.H., Rahim, M.Z.B., et al. "Intelligent approach for process modelling and optimization on electrical discharge machining of polycrystalline diamond", Journal of Intelligent Manufacturing, 31, pp. 227-247 (2020).
18. Moghaddam, M.A. and Kolahan, F. "Modeling and optimization of the electrical discharge machining process based on a combined artificial neural network and particle swarm optimization algorithm", Scientia Iranica, B, 27(3), pp. 1206-1217 (2020).
19. Sethuramalingam, P. and Sundararaj, O.N.R. "Adaptive neuro-fuzzy interference system modelling of EDM process using CNT infused copper electrode", International Journal of Intelligent Engineering and Systems, 10(3), pp. 102-116 (2017).
20. Fazlollahtabara, H. and Gholizadeh, H. "Fuzzy possibility regression integrated with fuzzy adaptive neural network for predicting and optimizing electrical discharge machining parameters", Computers & Industrial Engineering, 140, pp. 1-9 (2020).
21. Chau, N.L., Dao, T-P., and Nguyen, V.T.T. "Optimal design of a dragon y-inspired compliant joint for camera positioning system of nanoindentation tester based on a hybrid integration of Jaya-ANFIS", Mathematical Problems in Engineering, Article ID 8546095, pp. 1-16 (2018).
22. Chau, N.L., Le, H.G., Dao, T-P., et al. "Efficient hybrid method of FEA-based RSM and PSO algorithm for multi-objective optimization design for a compliant rotary joint for upper limb assistive device", Mathematical Problems in Engineering, Article ID 2587373, pp. 1-14 (2019).
23. Johnson, R.A. and Wichern, D.W., Applied Multivariate Statistical Analysis, Pearson Prentice Hall, Upper Saddle River, New Jersey (2007).
24. Hair, J.F., Black, W.C., Babin, B.J., et al., Multivariate Data Analysis, Prentice-Hall, New Jersey (2010).
25. Brown, M.T. and Tinsley, H.E.A. "Discriminant analysis", Journal of Leisure Research, 15(4), pp. 290-310 (1983).
26. Mansfield, E.R. and Helms, B.P. "Detecting multicollinearity", The American Statistician, 36(3a), pp. 158-160 (1982).
27. Eisenbeis, R.A. "Pitfalls in the application of discriminant analysis in business, finance, and economics", The Journal of Finance, 32(3), pp. 875-900 (1977).
28. Verma, J.P. "Application of discriminant analysis: For developing a classification model", In: Data Analysis in Management with SPSS Software, Springer, India, pp. 389-412 (2013).
29. McGarigal, K., Stafford, S., and Cushman, S. "Discriminant analysis", In: Multivariate Statistics for Wildlife and Ecology Research, Springer, New York, pp. 129-187 (2000).
30. Nath, R. and Pavur, R. "A new statistic in the oneway multivariate analysis of variance", Computational Statistics & Data Analysis, 2(4), pp. 297-315 (1985).
31. Brown, M.T. and Wicker, L.R. "Discriminant analysis", In: H.E.A. Tinsley and S.D. Brown, (Eds.), Handbook of Applied Multivariate Statistics and Mathematical Modeling, Academic Press, Elsevier, pp. 209-235 (2000).
32. Pituch, K.A. and Stevens, J.P., Applied Multivariate Statistics for the Social Sciences, Routledge, New York (2016).
33. Sarker, B. and Chakraborty S. "Discriminant analysisbased modeling of cotton fiber and yarn properties", Research Journal of Textile and Apparel, 26(1), pp. 18-40 (2021). https://doi.org/10.1108/RJTA-08-2020- 0092.
34. Singh, B., Kumar, J., and Kumar, S. "Investigating the influence of process parameters of ZNC EDM on machinability of A6061/10% SiC composite", Advances in Materials Science and Engineering, Article 173427, pp. 1-8 (2013).
35. Sahani, O.P., Kumar, R., and Vashista, M. "Effect of electro discharge machining process parameters on material removal rate", Journal of Basic and Applied Engineering Research, 1(2), pp. 17-20 (2014).
36. Puthumana, G. and Joshi, S.S. "Investigations into performance of dry EDM using slotted electrodes", International Journal of Precision Engineering and Manufacturing, 12(6), pp. 957-963 (2011).
37. Gaitonde, V.N., Manjaiah, M., Maradi, S., et al. "Multiresponse optimization in wire electric discharge machining (WEDM) of HCHCr steel by integrating response surface methodology (RSM) with differential evolution (DE)", In: J.P. Davim (Ed.), Computational Methods and Production Engineering, pp. 199-223 (2017).
38. Sultan, T., Kumar, A., and Gupta, R.D. "Material removal rate, electrode wear rate, and surface roughness evaluation in die sinking EDM with hollow tool through response surface methodology", International Journal of Manufacturing Engineering, Article 259129, pp. 1-16 (2014).