IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v212y2020ics0360544220318740.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220318740
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.118767?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Xiao, Qinge & Li, Congbo & Tang, Ying & Pan, Jian & Yu, Jun & Chen, Xingzheng, 2019. "Multi-component energy modeling and optimization for sustainable dry gear hobbing," Energy, Elsevier, vol. 187(C).
    3. 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.
    4. Cai, Wei & Lai, Kee-hung, 2021. "Sustainability assessment of mechanical manufacturing systems in the industrial sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    5. Shang, Zhendong & Gao, Dong & Jiang, Zhipeng & Lu, Yong, 2021. "A multi-perspective analysis of sustainability of machining processes based on a new extended virtual manufacturing framework," Energy, Elsevier, vol. 225(C).
    6. Cai, Wei & Wang, Lianguo & Li, Li & Xie, Jun & Jia, Shun & Zhang, Xugang & Jiang, Zhigang & Lai, Kee-hung, 2022. "A review on methods of energy performance improvement towards sustainable manufacturing from perspectives of energy monitoring, evaluation, optimization and benchmarking," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    7. 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.
    8. Cai, Wei & Li, Yanqi & Li, Li & Lai, Kee-hung & Jia, Shun & Xie, Jun & Zhang, Yuanhui & Hu, Luoke, 2022. "Energy saving and high efficiency production oriented forward-and-reverse multidirectional turning: Energy modeling and application," Energy, Elsevier, vol. 252(C).
    9. Hu, Luoke & Peng, Chen & Evans, Steve & Peng, Tao & Liu, Ying & Tang, Renzhong & Tiwari, Ashutosh, 2017. "Minimising the machining energy consumption of a machine tool by sequencing the features of a part," Energy, Elsevier, vol. 121(C), pages 292-305.
    10. Jessica Walther & Matthias Weigold, 2021. "A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry," Energies, MDPI, vol. 14(4), pages 1-24, February.
    11. Li, Lei & Huang, Haihong & Zou, Xiang & Zhao, Fu & Li, Guishan & Liu, Zhifeng, 2021. "An energy-efficient service-oriented energy supplying system and control for multi-machine in the production line," Applied Energy, Elsevier, vol. 286(C).
    12. Tangbin Xia & Xiangxin An & Huaqiang Yang & Yimin Jiang & Yuhui Xu & Meimei Zheng & Ershun Pan, 2023. "Efficient Energy Use in Manufacturing Systems—Modeling, Assessment, and Management Strategy," Energies, MDPI, vol. 16(3), pages 1-20, January.
    13. 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.
    14. Alexey I. Shinkevich & Tatiana V. Malysheva & Yulia V. Vertakova & Vladimir A. Plotnikov, 2021. "Optimization of Energy Consumption in Chemical Production Based on Descriptive Analytics and Neural Network Modeling," Mathematics, MDPI, vol. 9(4), pages 1-20, February.
    15. Gryboś, Dominik & Leszczyński, Jacek, 2023. "Exergy analysis of pressure reduction, back pressure and intermittent air supply configuration of utilization/expansion stage in compressed air systems," Energy, Elsevier, vol. 285(C).
    16. 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).
    17. Tavakoli, Sadi & Saettone, Simone & Steen, Sverre & Andersen, Poul & Schramm, Jesper & Pedersen, Eilif, 2020. "Modeling and analysis of performance and emissions of marine lean-burn natural gas engine propulsion in waves," Applied Energy, Elsevier, vol. 279(C).
    18. Xiao, Qinge & Li, Congbo & Tang, Ying & Li, Lingling & Li, Li, 2019. "A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning," Energy, Elsevier, vol. 166(C), pages 142-156.
    19. Liu, Conghu & Cai, Wei & Dinolov, Ognyan & Zhang, Cuixia & Rao, Weizhen & Jia, Shun & Li, Li & Chan, Felix T.S., 2018. "Emergy based sustainability evaluation of remanufacturing machining systems," Energy, Elsevier, vol. 150(C), pages 670-680.
    20. Cai, Wei & Liu, Conghu & Zhang, Cuixia & Ma, Minda & Rao, Weizhen & Li, Wenyi & He, Kang & Gao, Mengdi, 2018. "Developing the ecological compensation criterion of industrial solid waste based on emergy for sustainable development," Energy, Elsevier, vol. 157(C), pages 940-948.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:212:y:2020:i:c:s0360544220318740. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.