Multiobjective optimization of machining center process route: Tradeoffs between energy and cost

JOURNAL OF CLEANER PRODUCTION

卷:280

Article

Xiao, Yongmao^1,2  Zhang, Hua³  Jiang, Zhigang³  Gu, Quan⁴  Yan, Wei³ 

[1] Qiannan Normal Univ Nationalities, Sch Comp & Informat, Duyun 55800, Peoples R China

[2] Hubei Univ Automot Technol, Key Lab Automot Power Train & Elect, Shiyan 442002, Peoples R China

[3] Wuhan Univ Sci & Technol, Sch Machinery & Automat, Wuhan 430081, Hubei, Peoples R China

[4] Univ Glasgow, MRC, Glasgow G12 8QB, Lanark, Scotland

关键词: Process route;    Energy consumption;    Machining center;    Multi-objective optimization;    Genetic algorithm;   

DOI  :  10.1016/j.jclepro.2020.124171

来源: Elsevier

【 摘 要 】

Process route planning is vital for implementing energy saving and low-cost production in mechanical processing as it can directly affect the energy consumption and the cost of mechanical product processing. Therefore, a multiobjective optimization approach of machining center process routes to realize energy saving and low-cost mechanical processing is proposed in this paper. To provide theoretical support for this study, process route optimization problems of a machining center are analyzed, the concept of workstep element is introduced to represent the features of machined parts, and a multiobjective optimization model is established. The optimization model is solved based on the combination of a workstep chain intelligent generation algorithm and a non-dominated sorting genetic algorithm II. Finally, the emulsion pump case process route is used as a case study to verify the feasibility and practicability of the proposed method. Comparison with actual data shows that with the single objective of energy consumption and processing cost, based on the multiobjectives of energy saving and low cost as the optimization goal, the energy consumption was 1.018 x 10(7) J, and the processing cost was CNY32.65. Compared with the other two experimental results, the energy consumption and the processing cost demonstrated the best comprehensive performances, consistent with energy saving, low cost and sustainable production, thereby validating the established model. Furthermore, the optimization analysis shows that the combinatorial optimization algorithm has a better solution speed and optimization precision than the general genetic algorithm. (C) 2020 Elsevier Ltd. All rights reserved.

【 授权许可】

Free