Multi-objective optimization of cutting parameters for turning AISI 52100 hardened steel

Multi-objective optimization is becoming an essential stage in the choice of machining parameters. The objective of this paper was to optimize the choice of cutting parameters in terms of cutting speed, depth of cut, and feed rate during turning process of AISI 52100 hardened steel when multiple objectives were simultaneously taken into consideration like surface roughness, consumed power, cutting time or machining cost, productivity or metal removal rate and cutting forces. The turning process in this case was in dry conditions and the selected machining parameters have been investigated using full factorial design of experiments for three parameters (cutting speed, depth of cut, and feed rate). The relationship between parameters and performance responses were developed by using multiple linear regression analysis (MLR) and first-order empirical models were obtained. Analysis of variance (ANOVA) was employed to check the validity of the developed models within the limits of the factors that were being investigated and to test the significance of the above parameters. Thus, the obtained empirical models have been used to determine the optimal machining parameters with multi-objective optimization method based on weighting factors and genetic algorithm (GA) optimization method. Finally, an industrial example demonstrating the effectiveness of the proposed methodology was presented and confirmed the values when compared to the experimental results. This methodology should help the users to obtain the optimal process parameters for their application.