Optimization of Electrical Discharge Machining Parameters Using Artificial Neural Network with Different Electrodes

Electrical Discharge Machining (EDM) is a time consuming process and the operating cost is high. Optimum machining conditions reduces the machining time in the EDM process and yield better performances. Electrode material is also having their significance in the performances. In this paper a work has been carried out with different electrode materials namely copper, brass and tungsten while EDM of Al-SiCp Metal Matrix Composite. Material Removal Rate (MRR), Electrode Wear Rate (EWR) and Circularity (CIR) are considered as the performance measures. Artificial Neural Network is used for optimization of the machining parameters such as current, pulse on time and flushing pressure. Investigations indicate that the current is the most significant parameter. Among the three electrodes copper yields better performances. Machining time is reduced with better performances.