Machinability evaluation of Al–4%Cu–7.5%SiC metal matrix composite by Taguchi–Grey relational analysis and NSGA-II

Abstract

Machinability evaluation of Al–4%Cu–7.5%SiC metal matrix composite (MMC) prepared by powder metallurgy (P/M) process is presented. Specimens are prepared with 99.85% pure aluminum added with 4% copper and 7.5% silicon carbide particles by volume fraction. Scanning electron microscope image shows even distribution of particles in Al-MMC. Turning operation is performed by varying machining parameters and experiments are designed using Taguchi’s Design of Experiments (DoE), an L9 Orthogonal Array (OA) is chosen. A hybrid Taguchi–Grey relational approach is used to determine the optimum parameters over measured responses flank wear, roughness, and material removed. Analysis of Variance (ANOVA) result shows that the depth of cut is the influential parameter that contributes toward output responses. A metaheuristic evolutionary algorithm nondominated sorting genetic algorithm (NSGA-II) is applied to optimize the machining parameters for minimizing wear and maximizing metal removal. Experiments with optimum conditions show a better improvement in the output conditions.