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Energy efficiency evaluation for machining systems through virtual part

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  • Tuo, Junbo
  • Liu, Fei
  • Liu, Peiji
  • Zhang, Hua
  • Cai, Wei

Abstract

Energy prices, environmental concerns, carbon dioxide emissions, and economic matters are driving factors for research on reducing machining system energy consumption. Energy efficiency evaluation for machining systems is an effective management strategy for reducing the energy consumption and improving the energy efficiency of machining systems. This paper proposes a methodology called virtual part method to evaluate the energy efficiency of machining systems, and the proposed method overcomes the deficiencies or limitations of major existing methods (such as the entitative part method) through equivalence and virtualization of all possible machining system parts that may be manufactured in future. Based on an analysis of the energy compositions and characteristics of the proposed virtual part, an energy efficiency evaluation for machining systems through virtual part is conducted in three steps: 1) selection of evaluation indexes; 2) corresponding data collection for calculating indexes; and 3) development of energy efficiency evaluation system. Its application in a machine tool suggests that the proposed method is more accurate than the existing ones and contributes to energy-saving activities including the development of energy efficiency standards, design of energy-efficient machining systems, and reform of old machining systems.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:159:y:2018:i:c:p:172-183
    DOI: 10.1016/j.energy.2018.06.096
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    Cited by:

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    2. Zhaohui Feng & Xinru Ding & Hua Zhang & Ying Liu & Wei Yan & Xiaoli Jiang, 2023. "An Energy Consumption Estimation Method for the Tool Setting Process in CNC Milling Based on the Modular Arrangement of Predetermined Time Standards," Energies, MDPI, vol. 16(20), pages 1-18, October.
    3. Shun Jia & Qingwen Yuan & Wei Cai & Qinghe Yuan & Conghu Liu & Jingxiang Lv & Zhongwei Zhang, 2018. "Establishment of an Improved Material-Drilling Power Model to Support Energy Management of Drilling Processes," Energies, MDPI, vol. 11(8), pages 1-16, August.
    4. Jia, Shun & Cai, Wei & Liu, Conghu & Zhang, Zhongwei & Bai, Shuowei & Wang, Qiuyan & Li, Shuoshuo & Hu, Luoke, 2021. "Energy modeling and visualization analysis method of drilling processes in the manufacturing industry," Energy, Elsevier, vol. 228(C).
    5. Teng, Sin Yong & Touš, Michal & Leong, Wei Dong & How, Bing Shen & Lam, Hon Loong & Máša, Vítězslav, 2021. "Recent advances on industrial data-driven energy savings: Digital twins and infrastructures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).

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