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A multi-dimension coupling model for energy-efficiency of a machining process

Author

Listed:
  • Zhao, Junhua
  • Li, Li
  • Li, Lingling
  • Zhang, Yunfeng
  • Lin, Jiang
  • Cai, Wei
  • Sutherland, John W.

Abstract

Energy-efficient machining has become imperative for energy conservation of manufacturing sectors. The energy characteristics of machining process tend to be very complex, varying substantially with respect to different configurations of machine tool, workpiece and process parameters. This paper undertakes this challenge and explores the energy consumption characteristics of machining process adaptive to different machine tools, workpieces and process parameters. A multi-dimension coupling model of energy consumption for machining process is first established by considering specifications of machine tools, workpieces and processes. Then the influence factors of energy consumption are systematically analyzed from a multi-dimensional perspective. The internal interact relationship among each dimensional parameter is illustrated. To validate the effectiveness of the proposed energy model and determine the energy-efficient machining configurations with related to machine tools, workpieces and process parameters, a series of experiments are carried out on a CNC vertical machining center. Experimental results show that the optimal machining configurations can effectively reduce energy consumption and simultaneously improve energy-efficiency of CNC machining.

Suggested Citation

  • Zhao, Junhua & Li, Li & Li, Lingling & Zhang, Yunfeng & Lin, Jiang & Cai, Wei & Sutherland, John W., 2023. "A multi-dimension coupling model for energy-efficiency of a machining process," Energy, Elsevier, vol. 274(C).
  • Handle: RePEc:eee:energy:v:274:y:2023:i:c:s0360544223006382
    DOI: 10.1016/j.energy.2023.127244
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    References listed on IDEAS

    as
    1. Congbo Li & Lingling Li & Ying Tang & Yantao Zhu & Li Li, 2019. "A comprehensive approach to parameters optimization of energy-aware CNC milling," Journal of Intelligent Manufacturing, Springer, vol. 30(1), pages 123-138, January.
    2. Soheyl Khalilpourazari & Saman Khalilpourazary & Aybike Özyüksel Çiftçioğlu & Gerhard-Wilhelm Weber, 2021. "Designing energy-efficient high-precision multi-pass turning processes via robust optimization and artificial intelligence," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1621-1647, August.
    3. Hu, Luoke & Liu, Ying & Peng, Chen & Tang, Wangchujun & Tang, Renzhong & Tiwari, Ashutosh, 2018. "Minimising the energy consumption of tool change and tool path of machining by sequencing the features," Energy, Elsevier, vol. 147(C), pages 390-402.
    4. Lishu Lv & Zhaohui Deng & Can Yan & Tao Liu & Linlin Wan & Qianwei Gu, 2020. "Modelling and analysis for processing energy consumption of mechanism and data integrated machine tool," International Journal of Production Research, Taylor & Francis Journals, vol. 58(23), pages 7078-7093, December.
    5. Shang, Zhendong & Gao, Dong & Jiang, Zhipeng & Lu, Yong, 2019. "Towards less energy intensive heavy-duty machine tools: Power consumption characteristics and energy-saving strategies," Energy, Elsevier, vol. 178(C), pages 263-276.
    6. Zhang, Tao & Liu, Zhanqiang & Sun, Xiaodong & Xu, Jixiang & Dong, Longlong & Zhu, Genglei, 2020. "Investigation on specific milling energy and energy efficiency in high-speed milling based on energy flow theory," Energy, Elsevier, vol. 192(C).
    7. Cheng, Zhonghua & Li, Lianshui & Liu, Jun & Zhang, Huiming, 2018. "Total-factor carbon emission efficiency of China's provincial industrial sector and its dynamic evolution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 330-339.
    8. Liu, Wei & Li, Li & Cai, Wei & Li, Congbo & Li, Lingling & Chen, Xingzheng & Sutherland, John W., 2020. "Dynamic characteristics and energy consumption modelling of machine tools based on bond graph theory," Energy, Elsevier, vol. 212(C).
    9. Chen, Xingzheng & Li, Congbo & Tang, Ying & Li, Li & Du, Yanbin & Li, Lingling, 2019. "Integrated optimization of cutting tool and cutting parameters in face milling for minimizing energy footprint and production time," Energy, Elsevier, vol. 175(C), pages 1021-1037.
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