Hybrid RSM–NSGA-II based multi-objective optimization of electrical discharge machining of AISI D2 steel

Abstract

In this study, electrical discharge machining (EDM) of AISI D2 die steel was performed by varying three different process parameters: peak current (Ip), pulse-on time (Ton), and duty cycle (c). Enhancing both surface quality and machining performance is very important for die steel applications; therefore, a hybrid approach for multi-objective optimization was employed. A Box–Behnken design of response surface methodology (RSM) was utilized to conduct the experiments, while analysis of variance (ANOVA) was used to examine the influence of process parameters on the responses. Mathematical models were developed using RSM, which were finally utilized as fitness functions for the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to get solutions of multi-objective optimization. The algorithm generated a set of non-dominated solutions forming the Pareto frontier. To identify the most desirable solution, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used. The optimal results obtained through TOPSIS analysis were a surface roughness of 5.22 µm and a material removal rate (MRR) of 0.250 g/min, corresponding to the process parameters: peak current (Ip) = 10.03 A, pulse-on time (Ton) = 30.70 µs, and duty cycle (c) = 14.94%.