Cutting parameters optimization and constraints investigation for turning process by GA with self-organizing adaptive penalty strategy

For efficient use of machine tools at optimum cutting condition, it is necessary to find a suitable optimization method, which can find optimum feasible solution rapidly and explain the constraints as well. As the actual turning process parameter optimization is highly constrained and nonlinear, a modified Genetic Algorithm with Self Organizing Adaptive Penalty (SOAP) strategy is used to find the optimum cutting condition and to get clear idea of constraints at the optimum condition. Unit production cost is the objective function while limits of the cutting force, power, surface finish, stability condition, tool-chip interface temperature and available rotational speed in the machine tool are considered as the constraints. The result shows that our approach of GA with SOAP converges quickly by focusing on the boundary of the feasible and infeasible solution space created by constraints and also identifies the critical and non-critical constraints at the optimum condition.