Multi-objective genetic algorithm (MOGA) based optimization of high-pressure coolant assisted hard turning of 42CrMo4 steel

This study aims at finding a set of optimum solutions of cutting conditions for the machining responses of cutting temperature and surface roughness in hard turning of 42CrMo4 alloy steel at high-pressure coolant (HPC) condition. Comparative experimental investigations between dry and HPC cutting environments were performed to evaluate the stated responses concerning the factors of cutting speed, feed, and work-piece hardness. The full factorial method was employed for the experimental design. The measured value of cutting temperature and surface roughness was found in a reduced amount for HPC condition compared to dry cut for all of the machining runs. Empirical models were developed by response surface methodology for the responses of HPC-assisted machining. The ANOVA result indicated that cutting speed and hardness has the greatest effect on cutting temperature and surface roughness, respectively. Design of experiment (DoE) based optimization was carried out that results in the best optimum settings of 147 m/min cutting speed, 0.12 mm/rev feed rate and 42HRC work-piece hardness. Genetic algorithm based multi-objective optimization was then performed that simultaneously minimizes both of the response models. Within the constraints of experimental design, the optimal set resulted at the range of 86-165 m/min cutting speed, 0.12-0.13 mm/rev feed rate and HRC 42-44 work-piece hardness.