Optimization of micro milling of hardened steel with different grain sizes using multi-objective evolutionary algorithm

• The study integrated the LSM based on feed rate, cutting speed, and grain size providing the optimal process parameter.
• A genetic optimization algorithm was used to optimize the micro-milling process.
• The NSGA II algorithm was applied due to its coverage and easily to optimize machining process.
• The feed rate was the most significant factor for minimizing Fy force and Mz Torque.

Modern manufacturing processes need high production rates, low costs, and high product quality. Generally, surface roughness is a good reference to determine the performance in machined products. The use of optimization systems can determine the optimum machining parameters in the machining process, especially in milling operations. The present study integrates the least square model based on feed rate, cutting speed, and grain size with a genetic optimization algorithm to provide the optimal process parameter. The NSGA II algorithm was applied due to its coverage and easily to optimize the micro milling of hardened steel. The responses were Fy Force and Mz Torque. The results show that the feed rate was the most significant factor for minimizing Fy force and Mz Torque.