Parametric optimizing of electro-discharge machining for LM25Al/VC composite material machining using deterministic and stochastic methods

Abstract The electro-discharge machining (EDM) process is investigated using deterministic and stochastic methods to determine and model the effects of process parameters on machining performance. The workpiece utilized for the investigation was an LM25 aluminum alloy reinforced with vanadium carbide (VC), processed through a stir casting technique. EDM process parameters like peak current, discharge voltage, and pulse on-time are considered to analyze material removal rate, electrode wearing rate, and surface roughness. This study applied four multi-criteria decision-making (MCDM) and analytical methodologies to evaluate EDM performance. Then, the MCDM scores were compared using two objective verification mechanisms. In this case, the teaching-learning-based optimization (TLBO) technique delivered the best-desired results relative to the VIKOR, Grey relational grade (GRG), and the response surface method (RSM). Also, the RSM and analytical methods are simpler than the other methods, though they produced nearly identical results as the sophisticated MDCM and deterministic methods.