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Analysis of Multi-Objective Optimization of Machining Allowance Distribution and Parameters for Energy Saving Strategy

Author

Listed:
  • Keyan He

    (School of Intelligence and Technology, National University of Defense Technology, Changsha 410073, China)

  • Huajie Hong

    (School of Intelligence and Technology, National University of Defense Technology, Changsha 410073, China)

  • Renzhong Tang

    (Industrial Engineering Center, Zhejiang Province Key Laboratory of Advanced Manufacturing Technology, Zhejiang University, Hangzhou 310058, China)

  • Junyu Wei

    (School of Intelligence and Technology, National University of Defense Technology, Changsha 410073, China)

Abstract

Machining allowance distribution and related parameter optimization of machining processes have been well-discussed. However, for energy saving purposes, the optimization priorities of different machining phases should be different. There are often significant incoherencies between the existing research and real applications. This paper presents an improved method to optimize machining allowance distribution and parameters comprehensively, considering energy-saving strategy and other multi-objectives of different phases. The empirical parametric models of different machining phases were established, with the allowance distribution problem properly addressed. Based on previous analysis work of algorithm performance, non-dominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition were chosen to obtain Pareto solutions. Algorithm performances were compared based on the efficiency of finding the Pareto fronts. Two case studies of a cylindrical turning and a face milling were carried out. Results demonstrate that the proposed method is effective in trading-off and finding precise application scopes of machining allowances and parameters used in real production. Cutting tool life and surface roughness can be greatly improved for turning. Energy consumption of rough milling can be greatly reduced to around 20% of traditional methods. The optimum algorithm of each case is also recognized. The proposed method can be easily extended to other machining scenarios and can be used as guidance of process planning for meeting various engineering demands.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:2:p:638-:d:309044
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    References listed on IDEAS

    as
    1. Jia, Shun & Yuan, Qinghe & Lv, Jingxiang & Liu, Ying & Ren, Dawei & Zhang, Zhongwei, 2017. "Therblig-embedded value stream mapping method for lean energy machining," Energy, Elsevier, vol. 138(C), pages 1081-1098.
    2. 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.
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    Cited by:

    1. Wen Zhang & Qinghe Yuan & Shun Jia & Zhaojun (Steven) Li & Xianhui Yin, 2021. "Multi-Objective Optimization of Forth Flotation Process: An Application in Gold Ore," Sustainability, MDPI, vol. 13(15), pages 1-16, July.
    2. Shun Jia & Shang Wang & Jingxiang Lv & Wei Cai & Na Zhang & Zhongwei Zhang & Shuowei Bai, 2021. "Multi-Objective Optimization of CNC Turning Process Parameters Considering Transient-Steady State Energy Consumption," Sustainability, MDPI, vol. 13(24), pages 1-23, December.

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