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Pareto fronts of machining parameters for trade-off among energy consumption, cutting force and processing time

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  • He, Keyan
  • Tang, Renzhong
  • Jin, Mingzhou

Abstract

Cutting parameter optimization of machining processes is crucial for green manufacturing and needs to take energy consumption, cutting force and processing time into consideration. This paper presents a method to optimize machining parameters considering the trade-off between environmental concerns and economic objectives. The model for all three objectives of energy consumption, cutting force, processing time and their relationships with machining parameters is established based on theoretical analysis, experiment design, and statistical regression to obtain Pareto fronts. Various algorithms determining strategies, including sharing function approach, VEGA, NSGA-Ⅱ and MOEA/D, are used to study the Pareto front. Examples of a cylindrical turning and a face milling are used to conduct relative validation experiments to evaluate the proposed method and the computational performance of all algorithms. All of the experiments were conducted on a CK6153i lathe and an XHK-714F CNC machining center cutting C45E4 carbon steels. Results demonstrate that the proposed method is effective in finding trade-off among the three objectives and obtaining reasonable application ranges of machining parameters from Pareto fronts.

Suggested Citation

  • He, Keyan & Tang, Renzhong & Jin, Mingzhou, 2017. "Pareto fronts of machining parameters for trade-off among energy consumption, cutting force and processing time," International Journal of Production Economics, Elsevier, vol. 185(C), pages 113-127.
    • Handle: RePEc:eee:proeco:v:185:y:2017:i:c:p:113-127
    DOI: 10.1016/j.ijpe.2016.12.012
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    References listed on IDEAS

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    1. Konak, Abdullah & Coit, David W. & Smith, Alice E., 2006. "Multi-objective optimization using genetic algorithms: A tutorial," Reliability Engineering and System Safety, Elsevier, vol. 91(9), pages 992-1007.
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    1. Xiangxin An & Guojin Si & Tangbin Xia & Qinming Liu & Yaping Li & Rui Miao, 2022. "Operation and Maintenance Optimization for Manufacturing Systems with Energy Management," Energies, MDPI, vol. 15(19), pages 1-19, October.
    2. Cai, Wei & Liu, Fei & Xie, Jun & Liu, Peiji & Tuo, Junbo, 2017. "A tool for assessing the energy demand and efficiency of machining systems: Energy benchmarking," Energy, Elsevier, vol. 138(C), pages 332-347.
    3. Guo, Yuhan & Zhang, Yu & Boulaksil, Youssef & Qian, Yaguan & Allaoui, Hamid, 2023. "Modelling and analysis of online ride-sharing platforms – A sustainability perspective," European Journal of Operational Research, Elsevier, vol. 304(2), pages 577-595.
    4. Phuong Minh Khuong & Russell McKenna & Wolf Fichtner, 2020. "A Cost-Effective and Transferable Methodology for Rooftop PV Potential Assessment in Developing Countries," Energies, MDPI, vol. 13(10), pages 1-46, May.
    5. Guangdong Wu, 2017. "A Multi-Objective Trade-Off Model in Sustainable Construction Projects," Sustainability, MDPI, vol. 9(11), pages 1-18, October.
    6. Lijun Song & Jing Shi & Anda Pan & Jie Yang & Jun Xie, 2020. "A Dynamic Multi-Swarm Particle Swarm Optimizer for Multi-Objective Optimization of Machining Operations Considering Efficiency and Energy Consumption," Energies, MDPI, vol. 13(10), pages 1-18, May.
    7. Keyan He & Huajie Hong & Renzhong Tang & Junyu Wei, 2020. "Analysis of Multi-Objective Optimization of Machining Allowance Distribution and Parameters for Energy Saving Strategy," Sustainability, MDPI, vol. 12(2), pages 1-32, January.

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