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Multi-Objective Optimization of CNC Turning Process Parameters Considering Transient-Steady State Energy Consumption

Author

Listed:
  • Shun Jia

    (Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Shang Wang

    (Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Jingxiang Lv

    (Institute of Smart Manufacturing Systems, School of Construction Machinery, Chang’an University, Xi’an 710064, China)

  • Wei Cai

    (College of Engineering and Technology, Southwest University, Chongqing 400715, China)

  • Na Zhang

    (Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Zhongwei Zhang

    (School of Mechanical and Electrical Engineering, Henan University of Technology, Zhengzhou 450001, China)

  • Shuowei Bai

    (School of Mechanical and Electrical Engineering, Qingdao University, Qingdao 266071, China)

Abstract

Energy-saving and emission reduction are recognized as the primary measure to tackle the problems associated with climate change, which is one of the major challenges for humanity for the forthcoming decades. Energy modeling and process parameters optimization of machining are effective and powerful ways to realize energy saving in the manufacturing industry. In order to realize high quality and low energy consumption machining of computer numerical control (CNC) lathe, a multi-objective optimization of CNC turning process parameters considering transient-steady state energy consumption is proposed. By analyzing the energy consumption characteristics in the process of machining and introducing practical constraints, such as machine tool equipment performance and tool life, a multi-objective optimization model with turning process parameters as optimization variables and high quality and low energy consumption as optimization objectives is established. The model is solved by non-dominated sorting genetic algorithm-II (NSGA-II), and the pareto optimal solution set of the model is obtained. Finally, the machining process of shaft parts is studied by CK6153i CNC lathe. The results show that 38.3% energy consumption is saved, and the surface roughness of workpiece is reduced by 47.0%, which verifies the effectiveness of the optimization method.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13803-:d:702079
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    References listed on IDEAS

    as
    1. Shun Jia & Qinghe Yuan & Dawei Ren & Jingxiang Lv, 2017. "Energy Demand Modeling Methodology of Key State Transitions of Turning Processes," Energies, MDPI, vol. 10(4), pages 1-19, April.
    2. Richard Kminiak & Ladislav Dzurenda, 2019. "Impact of Sycamore Maple Thermal Treatment on a Granulometric Composition of Chips Obtained due to Processing on a CNC Machining Mentre," Sustainability, MDPI, vol. 11(3), pages 1-10, January.
    3. 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.
    4. Liu, Ying & Dong, Haibo & Lohse, Niels & Petrovic, Sanja, 2016. "A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance," International Journal of Production Economics, Elsevier, vol. 179(C), pages 259-272.
    5. 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.
    6. Jia, Shun & Cai, Wei & Liu, Conghu & Zhang, Zhongwei & Bai, Shuowei & Wang, Qiuyan & Li, Shuoshuo & Hu, Luoke, 2021. "Energy modeling and visualization analysis method of drilling processes in the manufacturing industry," Energy, Elsevier, vol. 228(C).
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