Combining the Entropy Method and Genetic Algorithm in the Multi-Objective Grinding Process

Grinding is a commonly used method for machining products that require high precision in the mechanical engineering industry. This study conducts multi-objective optimization of the grinding process for SUS440C steel on a surface grinding machine. A total of 15 experiments were designed by deploying the Box-Behnken method. In each experiment, the values of three cutting parameters, namely workpiece speed, feed rate, and depth of cutting, varied, while four objectives, involving surface roughness (Ra), cutting force component in the x-direction (Fx), cutting force component in the y-direction (Fy), and cutting force component in the z-direction (Fz), were measured. The entropy method was used to calculate the weights of the objectives, and the Genetic Algorithm (GA) was employed to solve the multi-objective optimization problem. According to the results, the optimal values of 5 m/min, 3 mm/stroke, and 0.0198 mm were, respectively, obtained for the workpiece speed, feed rate, and depth of cut. Corresponding to these cutting parameter optimal values, the values attained for the Ra, Fx, Fy, and Fz objectives were 0.612 mm, 10.126 N, 13.621 N, and 4.112 N, respectively.