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

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 L-27 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%.