Genetic algorithm-based burr size minimization in drilling of AISI 316L stainless steel

This study investigates the application of genetic algorithm (GA) for burr size minimization in drilling of AISI 316L stainless steel using HSS twist drills. Experiments were planned as per central composite rotatable design of experiments. The second order mathematical models for burr height and burr thickness were developed using response surface methodology (RSM) with cutting speed, feed, drill diameter, point angle and lip clearance angle as affecting parameters. The developed RSM models were then employed with GA, which is a search algorithm based on natural selection and natural genetics, to determine the optimal process parameters for a given drill diameter that results in minimum burr height and thickness. The simulation results reveal that point angle and cutting speed have significant effects in minimizing burr size.