Experimental investigation and optimization of EDM process parameters on EN31 steel using genetic algorithm

Electrical Discharge Machining (EDM) is the most preferred non-traditional material removal technique for hard materials. In the current research work focuses on electrical discharge machining process parameter optimization of EN31 steel. The selected important input parameters are current, pulse-on-time and gap voltage. These parameters were optimized using the genetic algorithm to get a better Material Removal Rate (MRR) reduced Tool Wear Rate (TWR) and surface roughness (Ra). ANOVA analyses were carried out to find the influences of individual parameters. Experiments were conducted as per L27 orthogonal array and genetic algorithm was used to analyze the effect of each parameter on the machining characteristics and to predict the optimal choice for each EDM parameter such as input current, gap voltage and pulse-on-time. The mathematical models were developed by using MINITAB software to find the response parameters and these models were the functions of application for genetic algorithm. Experimental results reveal that pulse on time is a more influenced parameter on MRR and TWR. The input current has the maximum effect on the surface roughness. Copyright (C) 2022 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advanced Materials for Innovation and Sustainability.