Optimization of machining parameters of GFRP pipes using evolutionary techniques

Near net shaped components of GFRP materials are obtained only through machining. This work is an attempt to optimize the machining parameters of GFRP pipes by evolutionary techniques. GFRP pipes made by both hand lay up as well as filament wound process are considered. Experiments were conducted based on taguchi’s technique and a combined objective is formed based on assumed weightage for individual parameters to minimize surface roughness, machining force and tool wear. In this paper the experimentally collected data are optimized through Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The results are analyzed and also compared with the traditional optimization technique.