Constrained multi-objective optimization of EDM process parameters using kriging model and particle swarm algorithm

This paper investigates the highly nonlinear relationship between process parameters and machining responses, including material removal rate (MRR), surface roughness (SR), and electrode wear rate (EWR) of electric discharge machining (EDM) using Kriging model. Subsequently, an emerging multi-objective optimization algorithm called particle swarm is used to determine the best machining conditions that not only maximize the machining speed but also minimize the EWR with a constraint of the SR. The experiment was carried out with P20 steel on a CNC EDM machine using copper electrode. The research result shows that the MRR increases sharply when increasing the discharge current just like other researches pointed out. However, the relationship between EWR and current is complicated. EWR appears the minimum value when the current is around 30?A. The speed of change of MRR per unit of EWR is the highest when the SR is around 14.5?µm. The combination of Kriging regression model and particle swarm optimization is considered as an intelligent process modeling and optimization of EDM machining. The proper selection of process parameters helps the EDM operator to reduce the machining time and cost.