OPTIMIZATION OF SURFACE ROUGHNESS AND MATERIAL REMOVAL RATE IN LASER BEAM MACHINING OF AL2O3 / CNT COMPOSITES USING PARTICLE SWAM OPTIMIZATION

Abstract

Alumia (Al₂O₃) based composites materials are widely used in variety of engineering applications due to its superior properties over the engineering materials. Optimization of surface roughness and material removal rate in machining is helpful to evaluate the process better responses. This paper aims to make use of particle swarm optimization method to optimize the surface roughness and material removal rate in laser beam machining of alumina (Al₂O₃) / Carbon nano tube (CNT) material composites. The control parameters considered are oxygen pressure, Pulse frequency, Cutting speed and wt.% of CNT. Experiments are planned and executed according to Taguchi’s L25 orthogonal array in design of experiments on an laser beam machining. A quadratic model was developed for surface roughness and material removal rate prediction using RSM. An attempt has been made to optimize the control parameters for the minimization of surface roughness and material removal rate using particle swarm optimization (PSO). The results indicates that the potential offered by PSO for finding the optimum control parameters for the minimization of surface roughness and maximum material removal rate.