Process Optimization and Estimation of Machining Performances Using Artificial Neural Network in Wire EDM

Abstract Wire Electrical Discharge Machining (WEDM) is a specialized thermal machining process capable of accurately machining parts with varying hardness or complex shapes, which have sharp edges that are very difficult to be machined by the main stream machining processes. This study outlines the development of model and its application to optimize WEDM machining parameters using the Taguchi's technique which is based on the robust design. Experimentation was performed as per Taguchi's L’16 orthogonal array. Each experiment has been performed under different cutting conditions of pulse-on, pulse-off, current, and bed speed. Among different process parameters voltage and flush rate were kept constant. Molybdenum wire having diameter of 0.18 mm was used as an electrode. Three responses namely accuracy, surface roughness, volumetric material removal rate have been considered for each experiment. Based on this analysis, process parameters are optimized. ANOVA is performed to determine the relative magnitude of the each factor on the objective function. Estimation and comparison of responses was done using artificial neural network.