Comparison of electrode wear in wire EDM for P-20, EN-19 and Stavax materials using artificial neural networks
by G. Ugrasen; H.V. Ravindra; G.V. Naveen Prakash; D.L. Vinay
International Journal of Precision Technology (IJPTECH), Vol. 4, No. 3/4, 2014

Abstract: This paper focuses on prediction and comparison of electrode wear during wire electrical discharge machining (WEDM) of P-20, EN-19 and Stavax tool steel materials. The control factors considered for the studies are pulse-on time, pulse-off time, current and bed speed. Process parameters have been selected based on Taguchi's L'₁₆ orthogonal array. Electrode wear prediction was carried out successfully for 50%, 60% and 70% of the training set for all the three materials using artificial neural networks (ANNs). For all the materials studied, 80-90% of the predicted values are within the 95% of measured values. Thus, predicted electrode wear of 70% training set correlates well with the measured electrode wear.

Online publication date: Sat, 28-Feb-2015

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