OPTIMIZING DRILLING INDUCED DELAMINATION IN GFRP COMPOSITES USING GENETIC ALGORITHM & PARTICLE SWARM OPTIMISATION

Composites are widely used in several applications ranging from automotive to aircraft industry due to their high strength to weight ratio. More often than not drilling on these composite laminates are conducted to serve some functional or aesthetic requirement. Delamination caused due to drilling pose a severe problem to the integrity of the structure. It is often not possible to develop an exact mathematical model to predict the delamination associated with such drilling. So, in this paper. an empirical model is developed based on the extensive experiments performed on polyester composite reinforced with chopped fibreglass. To account for the various parameters a Box-Behnken design of experiments is conducted for four parameters (material thickness, drill diameter, spindle speed, and feed rate) each having threedistinct levels. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) techniques are then used for predicting the global optimum (minimum delamination factor). The performance of both GA and PSO in terms of predicting the global optimum is found to be same. However, PSO converged much faster and required far lesser computational time.