Multiple Performance Optimization in Machining of GFRP Composites by a PCD Tool using Non-dominated Sorting Genetic Algorithm (NSGA-II)

Optimization of cutting parameters is important to achieving high quality in the machining process, especially where more complex multiple performance optimization is required. The present investigation focuses on the multiple performance optimization on machining characteristics of glass fiber reinforced plastic (GFRP) composites. The cutting parameters used for the experiments, which were carried out according to Taguchi's L-27, 3-level orthogonal array, were cutting speed, feed and depth of cut. Statistical models based on second-order polynomial equations were developed for the different responses. The Non-dominated Sorting Genetic Algorithm (NSGA-II) tool was used to optimize the cutting conditions, yielding a non-dominated solution set that is reported here.