Enhancement of EDM Machining Performance through Optimization of Process Parameters

Electrical Discharge Machining (EDM) is a non-traditional machining process widely used for shaping hard-to-machine conductive materials with high precision. The performance of EDM largely depends on several critical process parameters such as pulse current, pulse-on time, pulse-off time, dielectric fluid, tool material, and electrode polarity. This study focuses on enhancing the EDM process by optimizing these parameters to achieve improved material removal rate (MRR), surface finish, and tool wear rate (TWR). Advanced techniques such as Taguchi method, response surface methodology (RSM), and artificial intelligence approaches like genetic algorithm (GA) and artificial neural network (ANN) have been employed to model and predict the outcomes. Experimental investigations and optimization strategies reveal that proper parameter tuning can significantly enhance machining efficiency, reduce surface roughness, and extend tool life. This work contributes to the development of intelligent EDM process control for superior productivity and surface integrity in industrial applications.