Multi-Objective Optimization of Powder-Mixed Electro-Discharge Machining of Tool Steel Using Advanced Algorithm

Electrical discharge machining (EDM) technology is one of the unconventional machining processes with an ability to machine intricate geometrics with micro finishing. Powder-mixed EDM (PMEDM) extends the EDM process by adding conductive powder to the dielectric fluid to improve performance. This set of experiments summarizes the effect of brass and copper electrode on HcHcr D2 tool steel in chromium powder-mixed dielectric fluid. Powder concentration (PC), peak current (I), and pulse on-time (T_on) are considered as variable process parameters. General full factorial design of experiment (DOE) and ANOVA has been used to plan and analyze the experiments where powder concentration is observed as the most significant process parameter. The results also reveal that a brass electrode offers a high material removal rate (MRR). Whereas, the copper electrode has reported noteworthy improvement in surface roughness (Ra). Moreover, teaching–learning-based optimization (TLBO) algorithm has been used to optimize the developed multi-objective function assisted by the regression equations.