Modeling of metal removal rate in machining of aluminum matrix composite using artificial neural network

Abstract

Aluminum alloy reinforced with silicon carbide particles is a favored particulate metal–matrix composite which exhibits qualities like excellent strength-to-weight ratio, high thermal conductivity, hardness, and low coefficient of thermal expansion. Unfortunately, the same properties make them difficult both in manufacturing as well as machining. In this study, stir-cast A356/SiCp metal matrix composite is machined using electrochemical machining. Experiments are conducted by following Taguchi’s L27 orthogonal array design of experiments. Four independent variables, namely applied voltage, electrolyte concentration, electrode feed rate, and amount of reinforcement, are chosen and the metal removal rate is determined. A multilayer artificial neural network with back-propagation technique is employed to model the experimental data. A comparison made between predicted values and experimental values reveals a close matching with an average prediction error of 6.48%.