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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%.

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Aluminium metal matrix composites (AMMCs) are now gaining their usage in aerospace and automotive industries. Because of their inherent nature, difficult to machine, they find very little applications in other sectors. Even non traditional processes like Laser Jet Machining and Electro Discharge Machining result in significant sub surface damage to the work. In this paper, an attempt is made to machine the A356/SiC p composite work material using Electro Chemical Machining process. Silicon carbide with an average particle size of 40 microns is tried in three different proportions, namely 5%, 10% and 15% by weight. Taguchi's L 27 orthogonal array is chosen to design the experiments and 54 trials are conducted to study the effect of various parameters like applied voltage, electrolyte concentration, feed rate and percentage reinforcement on maximizing the material removal rate. ANOVA results have shown that all the four selected factors are significant and from the S/N graph the optimum level of each factor is chosen. A mathematical model is also developed using the regression method. Confirmation experiment is conducted and found that the data obtained have close match with the data predicted using the model.