Neural network–based modeling to predict residual stresses during electric discharge machining of Al/SiC metal matrix composites

Abstract

This article reports the result of an experimental study to analyze the effect of parameters that induce residual stresses during electric discharge machining of two particulate-reinforced metal matrix composites. Several factors were varied to study their impact on the formation of residual stresses, and pulse-off time was identified as the most significant factor. Artificial neural network was implemented to predict the residual stresses. Metal matrix composites with low coefficient of thermal expansion and high reinforced particle exhibit lower residual stresses. Also, better conductive electrode materials used during machining cause lower residual stress. The artificial neural network model accurately predicts the residual stresses and is a reliable tool for predicting residual stresses. The micrographs show that the workpiece with a higher concentration of reinforced particulates results in lesser flow line in the matrix material hence less residual stresses. X-ray spectra also reveal the phase transformation on the machined surface.