Multi-objective optimization of turning process of hardened material for energy efficiency

Improving the energy efficiency of machining processes is one way to reduce manufacturing costs in an effort to resolve environmental issues. The objective of this work is to optimize the machining parameters of the turning process for hardened AISI 4140 steel to reduce the consumed cutting energy and improve energy efficiency. The machining parameters evaluated include cutting speed, feed rate, nose radius, edge radius, rake angle, and relief angle. Firstly, numerical simulations were applied in conjunction with the Box- Behnken design (BBD) experimental method and response surface methodology (RSM) to render the relationships of the machining parameters with the specific required cutting energy as well as energy efficiency. Subsequently, a non-dominated sorting genetic algorithm-II (NSGA-II) was used to solve multi-objective optimization problems and search for Pareto optimal solutions. Finally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was adopted to determine the best solution compromised from the Pareto set. The results show that the specific required cutting energy decreased by approximately 16%, and the energy efficiency could be improved by about 11% compared to the non-optimized system. Therefore, this research is intended to contribute toward making machining processes of hardened steels more green and efficient.