Optimisation and prediction of machining parameters in EDM for Al-ZrO 2 using soft computing techniques with Taguchi method

G. Aswin Ramaswamy, Amal Krishna, M. Gautham, S.S. Sudharshan and J. Gokulachandran

International Journal of Process Management and Benchmarking, 2021, vol. 11, issue 6, 864-890

Abstract: In recent years, the usage of metal matrix composites has drastically increased in various engineering fields and hence the necessity for greater accuracy in machining of composites has also increased greatly. This study determines the optimal machining parameters viz., discharge current, pulse on time and voltage with respect to output performance such as material removal rate (MRR) and electrode wear rate (EWR) using electric discharge machine (EDM). Taguchi method is used for conducting experiments. Soft computing models such as artificial neural network (ANN) and fuzzy are developed to predict the process parameters. The developed models are validated with the experimental results. The results of both the models are also compared.

Keywords: optimisation; prediction; Taguchi method; artificial neural network; ANN; fuzzy logic. (search for similar items in EconPapers)
Date: 2021
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