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A novel approach for acquiring the real-time energy efficiency of machine tools

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  • Liu, Peiji
  • Liu, Fei
  • Qiu, Hang

Abstract

Machine tools (MT), as the key equipment of manufacturing industry, have enormous quantity and consume great amount of energy. In order to achieve consistent improvement of energy efficiency and environmental performance in manufacturing industry, it is significant to acquire the real-time energy efficiency (REE) of MT. However, due to the unavailable parameters and the limitations of using cutting-force-measuring instruments, it is difficult for existing methods to be applied to acquire the REE of MT. This paper proposes a novel approach for acquiring the REE without any usage of cutting-force-measuring instruments. To develop this approach, a model to characterize the relationship among the REE, input power and spindle speed of MT is established based on the basic data of MT. After obtaining these basic data, this approach only requires obtaining the spindle speed and input power to calculate the REE of MT. It has been tested and validated on a common used machine tool, and the results show its practicability and high level of accuracy for obtaining the REE of MT. This approach can be applied to assess the energy efficiency of MT, to support designers to design high-efficient MT and users to improve the energy efficiency of manufacturing process.

Suggested Citation

  • Liu, Peiji & Liu, Fei & Qiu, Hang, 2017. "A novel approach for acquiring the real-time energy efficiency of machine tools," Energy, Elsevier, vol. 121(C), pages 524-532.
    • Handle: RePEc:eee:energy:v:121:y:2017:i:c:p:524-532
    DOI: 10.1016/j.energy.2017.01.047
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    References listed on IDEAS

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    3. Tuo, Junbo & Liu, Fei & Liu, Peiji & Zhang, Hua & Cai, Wei, 2018. "Energy efficiency evaluation for machining systems through virtual part," Energy, Elsevier, vol. 159(C), pages 172-183.
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    5. 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.
    6. 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.
    7. 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).

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