Multi-objective optimization of cutting parameters for efficient and high-quality in milling gray cast iron HT250

This research focus on optimizing cutting parameters to realize high quality and efficiency in milling gray cast iron HT250. The experiments were designed by Box-Behnken design of response surface methodology and simulated in DEFORM-3D software, Second-order regression models of cutting temperature and surface roughness were obtained, which can predict the cutting temperature and surface roughness. Finally, the cutting parameters were optimized using a multi-objective genetic algorithm. The optimized results showed that the material removal rate increased by 49.28 %, while the cutting temperature decreased by 2.84% and the surface roughness decreased by 18.72 %. Additionally, the cutting temperature decreased by 20.02 % and the surface roughness 42.19 %, while the material removal rate increased by 7.78 %. According to the comprehensive study, the selection principle of cutting parameters for milling gray cast iron HT250 is as follows: use a low cutting speed, a large feed rate, a small depth of cut and a large width of cut.