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

Ge Cheng; Zilin Li; Xinjun Pei; Liangpeng Lin

doi:10.1117/12.3030164

5 June 2024 Multi-objective optimization of cutting parameters for efficient and high-quality in milling gray cast iron HT250

Ge Cheng, Zilin Li, Xinjun Pei, Liangpeng Lin

Author Affiliations +

Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131630D (2024) https://doi.org/10.1117/12.3030164
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ge Cheng, Zilin Li, Xinjun Pei, and Liangpeng Lin "Multi-objective optimization of cutting parameters for efficient and high-quality in milling gray cast iron HT250", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131630D (5 June 2024); https://doi.org/10.1117/12.3030164

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