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Dynamic characteristics and energy consumption modelling of machine tools based on bond graph theory

Author

Listed:
  • Liu, Wei
  • Li, Li
  • Cai, Wei
  • Li, Congbo
  • Li, Lingling
  • Chen, Xingzheng
  • Sutherland, John W.

Abstract

Fossil fuel depletion, air pollution, and climate change are imposing great pressure on industrial sectors, especially for manufacturing sectors. Energy consumption modelling is an important measure to promote the energy efficiency in manufacturing, which offers the fundamental basis for energy efficiency-related optimization. Although dynamic characteristics have a significant effect on operation of machine tools, traditional energy consumption models hardly take dynamic characteristics into consideration. This paper takes the feed system as an example and proposes a dynamic energy consumption model of machine tools with bond graph theory. Based on the structure of feed system, the proposed model is firstly expressed to a physical model and the bond graph model are established according to the law of energy conservation. Subsequently, with the augmented bond graph model, mathematical models of dynamic characteristics and energy consumption are proposed with state variables. Finally, the simulation and analysis of the proposed model are given. Results show that the proposed dynamic characteristics model and energy consumption model based on bond graph theory are reasonable and effective. Additionally, the proposed model can be used to explore the correlation between energy-consuming components and energy consumption of machine tools for realizing the high energy efficiency design of machine tools.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:212:y:2020:i:c:s0360544220318740
    DOI: 10.1016/j.energy.2020.118767
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    References listed on IDEAS

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    1. Schudeleit, Timo & Züst, Simon & Weiss, Lukas & Wegener, Konrad, 2016. "The Total Energy Efficiency Index for machine tools," Energy, Elsevier, vol. 102(C), pages 682-693.
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    3. Huang, Lin & Cheng, Gang & Zhu, Guoqing & Li, Dongliang, 2018. "Development of a bond graph based model library for turbocharged diesel engines," Energy, Elsevier, vol. 148(C), pages 728-743.
    4. Cai, Wei & Liu, Fei & Zhang, Hua & Liu, Peiji & Tuo, Junbo, 2017. "Development of dynamic energy benchmark for mass production in machining systems for energy management and energy-efficiency improvement," Applied Energy, Elsevier, vol. 202(C), pages 715-725.
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    Citations

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    Cited by:

    1. 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).
    2. Wang, Jinling & Tian, Yebing & Hu, Xintao & Han, Jinguo & Liu, Bing, 2023. "Integrated assessment and optimization of dual environment and production drivers in grinding," Energy, Elsevier, vol. 272(C).
    3. Qian Liu & Zhuxin Zhang & Tuo Jia & Lixin Wang & Dingxuan Zhao, 2022. "Energy Consumption Analysis of Helicopter Traction Device: A Modeling Method Considering Both Dynamic and Energy Consumption Characteristics of Mechanical Systems," Energies, MDPI, vol. 15(20), pages 1-20, October.
    4. Wang, Liping & Wei, Pengxuan & Li, Weitao & Du, Li, 2024. "Modelling and optimization method for energy saving of computer numerical control machine tools under operating condition," Energy, Elsevier, vol. 306(C).
    5. Bermeo-Ayerbe, Miguel Angel & Ocampo-Martinez, Carlos & Diaz-Rozo, Javier, 2022. "Data-driven energy prediction modeling for both energy efficiency and maintenance in smart manufacturing systems," Energy, Elsevier, vol. 238(PB).

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