Bi-objective Optimization of an EDM Process for Monel K-500 Alloy Using RSM-MOGA

In this current research work, Electro Discharge Machining (EDM) was performed on Monel K-500 alloy. The experiment utilized a Box–Behnken design matrix, wherein four input variables, namely peak-current denoted as Ip, pulse-on-time denoted as Ton, and duty-cycle denoted as Tau, and servo voltage denoted as SV, were varied. The rate of removal of workpiece material, usually denoted as MRR, and the removal of tool material, typically denoted as EWR, were considered as output responses. With the utilizing the experimental data second-order mathematical prediction model for MRR and EWR was developed by using Response surface methodology (RSM). R2 value was found to be 99.40% and 96.60% for MRR and EWR RSM-based prediction model, respectively. High value of R2 indicated good adequacy for prediction. The mathematical model is utilized within the Multi-Objective Genetic Algorithm (MOGA) to determine the optimal process parameters based on two decision criteria (maximum MRR and minimum EWR) for Electro Discharge Machining (EDM). The MOGA proves to be a highly effective technique for attaining optimal solutions based on multiple decision criteria. Using this technique, non-dominated solutions were identified and the Pareto frontier was established.