Commenced in January 2007
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Edition: International
Paper Count: 30075
Optimization of Machining Parametric Study on Electrical Discharge Machining

Authors: Rakesh Prajapati, Purvik Patel, Hardik Patel

Abstract:

Productivity and quality are two important aspects that have become great concerns in today’s competitive global market. Every production/manufacturing unit mainly focuses on these areas in relation to the process, as well as the product developed. The electrical discharge machining (EDM) process, even now it is an experience process, wherein the selected parameters are still often far from the maximum, and at the same time selecting optimization parameters is costly and time consuming. Material Removal Rate (MRR) during the process has been considered as a productivity estimate with the aim to maximize it, with an intention of minimizing surface roughness taken as most important output parameter. These two opposites in nature requirements have been simultaneously satisfied by selecting an optimal process environment (optimal parameter setting). Objective function is obtained by Regression Analysis and Analysis of Variance. Then objective function is optimized using Genetic Algorithm technique. The model is shown to be effective; MRR and Surface Roughness improved using optimized machining parameters.

Keywords: Material removal rate, TWR, OC, DOE, ANOVA, MINITAB.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130237

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References:


[1] Cao, F.G., and Yang, D.Y., 2004, “The study of high efficiency and intelligent optimization system in EDM sinking process,” Journal of Materials Processing Technology, 149(1-3), pp. 83-87.
[2] Lee H.T, Hsu F.C., and Tai T.Y., 2004, “Study of surface integrity using the small area EDM process with a Copper-Tungsten electrode,” Material Science and Engineering, A364, pp. 346-356.
[3] Dew-angan, S., Datta, S., Patel, S.K., and Mahapatra S.S., 2011, “A case study on quality and productivity optimization in electric discharge machining,” 14th International Conference in Advanced Materials and Processing Technologies AMPT201113-16 July, Istanbul, Turkey.
[4] Joshi, S, N., and Pande, S.S., 2011, “Intelligent process modeling and optimization of die-sinking electric discharge machining,” Elsevier, 11(2), pp. 2743–2755.
[5] Mendel, D., Pal, S.K., and Saha, P., 2007, “Modeling of electrical discharge machining process using back propagation neural network and multi-objective optimization using non-dominating sorting genetic algorithm-II,” Journal of Materials Processing Technology, 186(1-3), pp. 154-162.
[6] American Supplier Institute Inc (ASI), 1989, "Taguchi Methods: Implementation Manual", ASI, Dearborn, MI.
[7] Bendell, A., 1988, "Introduction to Taguchi Methodology", Taguchi Methods: Proceedings of the 1988 European Conference, Elsevier Applied Science, London, England, pp. 1-14.
[8] Bryn, D., M. and Taguchi, S., 1986, "The Taguchi Approach to Parameter Design", ASQC Quality Congress Transactions, Anaheim, CA, p 168.
[9] Kackar, Raghu, 1985, "Off-Line Quality Control, Parameter Design, and the Taguchi Method", Journal of Quality Technology, Vol. 17, No.4, pp. 176-188.
[10] Sullivan, L. P., 1987. "The Power of Taguchi Methods", Quality Progress, June, pp 76-79.
[11] Phadke, S. M., 1989. Quality Engineering Using Robust Design, Prentice Hall, Englewood Cliffs, N.J.