Optimization of Machining Parameters for Aluminum and Silicon Carbide Composite Using Genetic Algorithm

Abstract

Aluminium matrix particulate composites are one of the materials finding wide ranging applications in automobile, aerospace and military industries because of their attractive properties such as high strength to weight ratio, high wear resistance, high temperature stability, etc Though most engineering components aluminium matrix particulate composites are primarily manufactured in near net shape, machining of Metal matrix composites (MMCs) have joined the group of difficult-to-cut materials because of the inherent abrasiveness of ceramic reinforcements. The main objective of this paper is to study of drilling of Aluminium Silicon Carbide (AlSiC) is investigated. Optimum machining condition for maximizing metal removal rate and minimizing the surface roughness is determined using desirability function approach. The influences of different parameters in machining Al/SiC particulate composite have been analyzed in detail. Aluminium hybrid composites reinforced with Silicon carbide particulates were fabricate by stir casting methods. Different volume fractions of SiC (10, 15 & 20% vol) were used for synthesis. This paper attempts to establish a comprehensive mathematical model for correlating the interactive and higher-order influences of various machining parameters using Taguchi method with an L 27 fractional factorial design were selected for the present experiment to obtain the optimal settings of factors and study their effects on multiple performance characteristics. Analysis of variance (ANOVA) has been performed to verify the fit and adequacy of the developed mathematical models. In the present work, a multiple regression model is used to represent relationship between input and output variables and a multi-objective optimization method based on a Genetic Algorithm (GA) is used to optimize the process.