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

• Published in: Journal of cleaner production Vol. 280; p. 124171
• Main Authors: Xiao, Yongmao, Zhang, Hua, Jiang, Zhigang, Gu, Quan, Yan, Wei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 20.01.2021
• Subjects: algorithms; case studies; emulsions; energy; energy conservation; Energy consumption; Genetic algorithm; Machining center; Multi-objective optimization; planning; Process route; Energy consumption; Multi-objective optimization; Genetic algorithm; Process route; Machining center
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2020.124171

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 × 107 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. •Analyzed the underlying effects of the mechanism of process route optimization.•A multiobjective optimization model of the machining center process route was built.•The concept of workstep element was proposed.•An intelligent combinatorial optimization algorithm for the workstep chain was proposed.