Multi Objective Optimization of Drilling Process Variables Using Genetic Algorithm for Precision Drilling Operation

The aim of this paper is to utilise genetic algorithm approach to investigate the effect of CNC drilling process variables such as spindle speed, drill diameter, material thickness, and feed rate on thrust force and torque generated during the drilling of mild steel plate using H.S.S drill. To find out the relationship between drilling process variable on thrust force and torque generated to the jig and work table, multiple regression model is used. Regression model is generated with the help of SPSS19. Statistical validity, explanatory power and significance of the regression model is tested at 95% confidence interval. High degree of correlation between drilling parameters and thrust force/torque has been found with almost negligible interaction amongst the drilling process parameter. Regression model is found to be significant and valid. Optimum combination of process variable to explain thrust force and torque generated is found with the help of MATLAB solver using genetic algorithm. Sensitivity analysis investigates the change in the solutions resulting from making changes in parameters of the GA model. In this research, sensitivity analysis shows how sensitive of solutions and decision variables to changes in weights in objective functions. It shows that the solutions of an aggregation method are affected by weight adjustment. Thus, in case of aggregation method, if the weights are not appropriately assigned, the GA may not give out good solutions. On the other hand, for the proposed Pareto method, it is not sensitive to weigh, so incorrect weights do not affect the solution outcome of Pareto based MOGA.