Improving product quality is a crucial factor in determining the competitiveness and business efficiency of enterprises. This study investigates the influence of the cutting parameters, including the cutting speed, the depth of cut, and the feed rate on the surface roughness and the residual stress during the turning of AISI 304 austenitic stainless steel. Moreover, the work aims to determine optimal cutting parameters to satisfy both surface roughness and residual stress requirements. The mathematical model of the relationship between the machining parameters and the performance characteristics was formulated based on the response surface methodology (RSM) and the Box-Behnken design of the experiments. Pareto optimal solution applying natural-inspired algorithm (Bat Algorithm) is proposed to solve the bi-objective optimization problem to obtain the lowest surface roughness and minimal residual stress. The optimum cutting parameters selected by the manufacturing planners from the Pareto optimal fronts are calculated to comply with the production requirements.
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Multi-Objective Optimization for Turning Process of 304 Stainless Steel Based on Dung Beetle Optimizer-Back Propagation Neural Network and Improved Particle Swarm Optimization Huan Xue, Tao Li, Jie Li, Yansong Zhang, Shiyao Huang, Yongchun Li, Chongwen Yang, Wenqian Zhang Journal of Materials Engineering and Performance.2024; 33(8): 3787. CrossRef