Research on Energy Saving, Low-Cost and High-Quality Cutting Parameter Optimization Based on Multi-Objective Egret Swarm Algorithm

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Detalhes bibliográficos
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
Assuntos:	Competitiveness Cutting speed Mathematical models Algorithms Numerical controls Machine tools Cutting tools Multiple objective analysis Manufacturing Production costs Energy consumption Optimization models Energy conservation Product quality Energy costs Genetic algorithms Machining Effectiveness Cutting parameters Variables Energy efficiency Surface roughness Basic converters Feed rate Optimization algorithms
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Descrição
Resumo:	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.
ISSN:	2227-9717
DOI:	10.3390/pr13082390
Fonte:	Materials Science Database