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

• Published in: International journal on interactive design and manufacturing Vol. 16; no. 3; pp. 1253 - 1272
• Main Authors: Saha, Shanta, Zaman, Prianka B., Tusar, Md. Imran Hasan, Dhar, Nikhil R.
• Format: Journal Article
• Language: English
• Published: Paris Springer Paris 01-09-2022; Springer Nature B.V
• Subjects: Alloy steels; CAE) and Design; Chromium molybdenum steels; Computer-Aided Engineering (CAD; Constraint modelling; Coolants; Cooling; Cutting parameters; Cutting speed; Cutting tools; Design of experiments; Design optimization; Electronics and Microelectronics; Engineering; Engineering Design; Feed rate; Genetic algorithms; Hardness; High pressure; Industrial Design; Instrumentation; Mechanical Engineering; Multiple objective analysis; Optimization; Residual stress; Response surface methodology; Steel; Surface roughness; Technical Paper; Turning (machining); Workpieces; MOGA; High-pressure coolant; Surface roughness; RSM; Cutting temperature; Hard turning
• ISSN: 1955-2513; 1955-2505
• DOI: 10.1007/s12008-022-00848-7

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.