Multi-objective optimization of turning process for hardened material based on hybrid approach

Abstract

Energy and environmental issues have become pertinent to all industries in the globe because of sustainable development issues. This paper systematically investigates the turning process of the hardened material via process modeling, numerical experiments, and a hybrid algorithm. The objectives of this work are to reduce the specific cutting energy (SCE) and improve the energy efficiency (EF) based on the turning conditions optimization. The machining simulations were performed in conjunction response surface methodology (RSM) to generate the quadratic mathematical models of the specific cutting energy and energy efficiency in terms of machining parameters, including cutting speed, feed rate, nose radius, edge radius, and rake angle. An analysis of variance (ANOVA) was then adopted to examine the model adequacy and significant parameters. Subsequently, an evolutionary algorithm, namely non-dominated sorting genetic algorithm-II (NSGA-II) was used to find a much better spread of design solutions and better convergence near the true Pareto optimal front. A quantitative approach, namely entropy method was conducted to calculate the weight factors of multiple responses. In the last step, a TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) was applied as to determine the best compromise solution. It was indicated that the energy efficiency was significantly improved using the optimal machining parameters and the specific cutting energy was effectively decreased in comparison with initial values. Moreover, the integrative approach performed very well in optimum performance of the machining process. Therefore, this work is expected as a contribution to improve the machining efficiency of the turning process of hardened steels.