Multi-objective optimization of steel AISI 1040 dry turning using genetic algorithm

This study investigated the AISI 1040 steel turning in dry environment with four cutting inserts of different corner radii coated by CVD method. Experimental investigations were performed for different levels of cutting speeds, feeds and depths of cut using a randomized full factorial design. Quality characteristics of the workpiece machined surface were measured (arithmetical mean roughness) as well as the cutting inserts tool life characteristics (average width of flank wear). Machining times and chip volume were calculated, and based on this, chip quantity in time (material removal rate). The response surface approach and analysis of variance were used to determine the effects of input process parameters on the response variables. Based on the derived regression models, multi-objective optimization of output process parameters was performed using genetic algorithm. The objective function was simultaneous minimization of flank wear, minimization of surface roughness and maximization of material removal rate. The parameters of the genetic algorithm (crossover ratio, crossover fraction, mutation rate, Pareto front population fraction) were varied to obtain the optimal values of the objective function. Additionally, a sensitivity analysis was performed, which showed that the selected values of genetic algorithm parameters gave the best (minimum) value of objective function. Instead of the usual approach of obtaining only one combination of optimal parameters as a final solution, the basic idea was to obtain multiple combinations of optimal input process parameters depending on the importance of each output process parameter, i.e. requirements of production. Accordingly, the results of multi-objective optimization showed that there are a large number of Pareto optimal solutions. To validate the optimal input and output process values, confirmation experiments were conducted for selected trials of Pareto optimal results obtained from multi-objective optimization. A mean error percentage of 1.478% and 1.146% for flank wear and arithmetical mean roughness, respectively, proves that the predicted optimum values are confirmed by experimental results.