Optimization of Intervening Variables in MicroEDM of SS 316L using a Genetic Algorithm and Response-Surface Methodology

Abstract

This research paper attempts to investigate the optimum values of the major intervening parameters in micro-Electric Discharge Machining (microEDM) of Stainless Steel (SS) 316L. Experiments are conducted using a 400 micrometre brass electrode. The discharge current, pulseon time and pulse-off time with three levels are selected as significant intervening parameters. The Taguchi method is initially applied to determine the optimum process parameters and the number of experiments required to model the responses. The response-surface methodology (RSM) is applied to correlate between intervening parameters, and the selected objectives to maximize the material removal rate (MRR) and to minimize the tool wear rate (TWR) in the machining of SS 316 L. The mathematical model obtained from RSM is used as a fitness function to multi-objective optimization using a genetic algorithm (GA). The results reveal that the resulting optimal intervening parameters improve the chosen objectives significantly. The confirmation results prove that the better developed mathematical model yields deviate within 5% of the experiment.