Experimental Investigation of Machinability on Hybrid Metal Matrix Composites using Particle Swarm Optimization

Abstract

The manufacturing sector is a scoring sector for contribution to the economy of any industry. Especially industries leading with a processing of materials have a higher proportionate need to concentrate on its performance growth in relation with material machining and its quality. Hence, the machinability plays a significant role and its optimum has to be determined. Aluminium hybrid particulate reinforced composites has demand in growing fields such as automotive, aircraft and locomotive industries. The research work focuses on machinability and optimization of machining parameters on Aluminium 7075 alloy reinforced with alumina and silicon carbide, known as hybrid aluminium composites. Three specimens A, B, C with 5% alumina as constant for all specimens, but silicon carbide with 5%, 10%, and 15% respectively, were fabricated using stir casting. Based upon Box-Behnken design, 27 experiments were conducted in universal milling machine using carbide coated cutting tool inserts. The machining characteristics such as surface roughness and material removal rate, cutting force and tool wear were studied simultaneously. The input parameters such as spindle speed, feed rate, depth of cut and the weight percentage of SiC were optimized using particle swarm optimization (PSO) for multi-objectives. The results were verified through confirmatory tests.