Machinability Analysis and Multiple Performance Optimization During Laser Micro-drilling of CNT Reinforced Polymer Nanocomposite

The present research focuses on presaging the optimal parametric data set (material removal rate (MRR), taper, and heat-affected zone (HAZ)) for laser micro-drilling of a new class of polymer composite consisting of carbon nanotube (CNT) and epoxy. Experiments are conducted according to Taguchi’s methodology with cutting speed, lamp current, air pressure, and pulse width as controllable parameters and their significance is assessed through ANOVA. Second-order nonlinear regression models integrating relationship between control parameters and output responses are developed for optimizing the process parameters. Optimum process parameters are evaluated to minimize taper and HAZ and maximize the MRR. These nonlinear regression equations are further utilized in accelerated particle swarm optimization (APSO) algorithm for single as well as multiple performance optimization. A confirmation test is carried out with the optimal parameter settings obtained from Taguchi’s methodology and APSO and improvement in performance parameter is noticed in each case. It is also observed that the APSO metaheuristic algorithm performs efficiently for optimizing the responses relating to the laser micro-drilling process of nano-composites both in individual and multi-objective optimization.