Research on Energy Saving, Low-Cost and High-Quality Cutting Parameter Optimization Based on Multi-Objective Egret Swarm Algorithm
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| Publicado no: | Processes vol. 13, no. 8 (2025), p. 2390-2406 |
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| Autor principal: | |
| Outros Autores: | , |
| Publicado em: |
MDPI AG
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| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 024 | 7 | |a 10.3390/pr13082390 |2 doi | |
| 035 | |a 3244058015 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231553 |2 nlm | ||
| 100 | 1 | |a Zheng Yanfang |u School of Safety and Management Engineering, Hunan Institute of Technology, Hengyang 421002, China; yanfangzheng@hnit.edu.cn | |
| 245 | 1 | |a Research on Energy Saving, Low-Cost and High-Quality Cutting Parameter Optimization Based on Multi-Objective Egret Swarm Algorithm | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a In the process of CNC machining, reducing energy consumption, production costs, and improving machining quality are critical strategies for enhancing product competitiveness. Based on an analysis of machine tool processing mechanisms, calculation models for energy consumption, manufacturing cost, and quality (represented by surface roughness) in CNC lathes were established. These models were optimized using the Egret Swarm Optimization Algorithm (ESOA), which integrates three core strategies: waiting, random search, and bounding mechanisms. With the objectives of minimizing energy consumption, manufacturing cost, and maximizing quality, cutting parameters (e.g., cutting speed, feed rate, and depth of cut) were selected as optimization variables. A multi-objective ESOA (MOESOA) framework was applied to resolve trade-offs among conflicting objectives, and the effectiveness of the proposed method was validated through a case study. The simulation results show that the optimization of cutting parameters is beneficial to energy conservation during the machining process, although it may increase costs. Additionally, under the three-objective optimization, the improvement of surface roughness is relatively limited. The further two-objective (energy consumption and cost) optimization model demonstrates better convergence while ensuring that the surface roughness meets the basic requirements. This method provides an effective tool for optimizing cutting parameters. | |
| 653 | |a Competitiveness | ||
| 653 | |a Cutting speed | ||
| 653 | |a Mathematical models | ||
| 653 | |a Algorithms | ||
| 653 | |a Numerical controls | ||
| 653 | |a Machine tools | ||
| 653 | |a Cutting tools | ||
| 653 | |a Multiple objective analysis | ||
| 653 | |a Manufacturing | ||
| 653 | |a Production costs | ||
| 653 | |a Energy consumption | ||
| 653 | |a Optimization models | ||
| 653 | |a Energy conservation | ||
| 653 | |a Product quality | ||
| 653 | |a Energy costs | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Machining | ||
| 653 | |a Effectiveness | ||
| 653 | |a Cutting parameters | ||
| 653 | |a Variables | ||
| 653 | |a Energy efficiency | ||
| 653 | |a Surface roughness | ||
| 653 | |a Basic converters | ||
| 653 | |a Feed rate | ||
| 653 | |a Optimization algorithms | ||
| 700 | 1 | |a Xiao Yongmao |u School of Navigation, Jiujiang Polytechnic University of Science and Technology, Gongqingcheng 332020, China | |
| 700 | 1 | |a Zhu, Xiaoyong |u School of Economics and Management, Shaoyang University, Shaoyang 422000, China; zhuxysyu@126.com | |
| 773 | 0 | |t Processes |g vol. 13, no. 8 (2025), p. 2390-2406 | |
| 786 | 0 | |d ProQuest |t Materials Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3244058015/abstract/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3244058015/fulltextwithgraphics/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3244058015/fulltextPDF/embedded/J7RWLIQ9I3C9JK51?source=fedsrch |