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Modelling and analysis for processing energy consumption of mechanism and data integrated machine tool

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  • Lishu Lv
  • Zhaohui Deng
  • Can Yan
  • Tao Liu
  • Linlin Wan
  • Qianwei Gu

Abstract

Reducing the energy consumption of machine tool processing has been a consistent concern and research issue in the international manufacturing industry. To achieve energy conservation and emissions reduction in machine tools, an energy consumption model of the machining process must first be established. However, considering the differences in machining equipment, complex energy flow conditions and time-varying load forces, accurate energy consumption of machining process can be difficult to obtain. Against this backdrop, our research proposes a modelling method for processing energy consumption with an integration mechanism and data, that considers the advantages of mechanism analysis modelling and data modelling. Among them, the mechanism analytical model for characterising energy consumption is determined by the dynamic mechanism of the multi-energy source of the machine tool. The data model is built using a support vector machine (SVM) algorithm based on the deviation between the actual results and the theoretical model. Then, a case study is performed to verify the feasibility and practicability of the proposed method. The results demonstrate accurate prediction and quantitative analysis of energy consumption.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:23:p:7078-7093
    DOI: 10.1080/00207543.2020.1756508
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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).

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