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
Autor principal: Zheng Yanfang
Outros Autores: Xiao Yongmao, Zhu, Xiaoyong
Publicado em:
MDPI AG
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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