Multiple Performance Optimization in Machining of Ud-gfrp Composites by a Pcd Tool Using Distance – Based Pareto Genetic Algorithm (dpga)

Optimization of cutting parameters is important to achieve high quality in the machining process, especially where more complex multiple performance optimizations are required. The present investigation focuses on the multiple performance optimizations of machining characteristics of unidirectional glass fiber reinforced plastic (UD-GFRP) composites. The cutting parameters used for the experiments, were carried out according to Taguchi’s L18, mixed-level orthogonal array. The parameters chosen are tool rake angle, tool nose radius, feed rate, cutting speed, cutting environment (dry, wet and cooled) and depth of cut. Statistical model based on second order polynomial equations were developed for the two responses. The Distance – Based Pareto Genetic Algorithm (DPGA) tool was used to optimize the cutting conditions.