Optimization and Prediction of Machining Responses Using Response Surface Methodology and Adaptive Neural Network by Wire Electric Discharge Machining of Alloy-X

Abstract

Wire electric discharge machining non-contact machining process based on spark erosion technique. It can machine difficult-to-cut materials with excellent precision. In this paper Alloy-X, a nickel-based superalloy was machined at different machining parameters. Input parameters like pulse on time, pulse off time, servo voltage and wire feed were employed for the machining. Response parameters like cutting speed and surface roughness were analyzed from the L25 orthogonal experiments. It was noted that the pulse on time and servo voltage were the most influential parameters. Both cutting speed and surface roughness increased on increase in pulse on time and decrease in servo voltage. Grey relation analysis was performed to get the optimal parametric setting. Response surface method and artificial neural network predictors were used in the prediction of cutting speed and surface roughness. It was found that among the two predictors artificial neural network was accurate than response surface method.