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Energy modelling and energy saving strategy analysis of a machine tool during non-cutting status

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  • Xiaona Luan
  • Song Zhang
  • Jie Chen
  • Gang Li

Abstract

Increasing energy cost and environmental problems push forward research on energy modelling and saving strategy in the sustainable manufacturing field. A manufacturing process usually includes cutting process and non-cutting status. Energy consumption of non-cutting status accounts for a large amount of the total energy consumption during a machining process. This paper focuses on the energy modelling and saving potential analysis in non-cutting status. First, power of non-cutting status was modelled, which included the fixed power, spindle idle power, feed motion power and rapid feed power. Secondly, milling experiments were conducted to study the characteristic and modelling method of the non-cutting status power. The experiments were divided into two types, some experiments were used to calculate the coefficients, and the others were applied to verify the proposed model. Finally, regression analysis and Analysis of Variance (ANOVA) were applied to illustrate the prediction accuracy of the proposed model. The energy saving strategy was developed for the non-cutting status, which includes shortening the air cutting time and not frequently changing the spindle speed to avoid a power peak. It indicates that the proposed model can predict the power consumption of non-cutting status accurately.

Suggested Citation

  • Xiaona Luan & Song Zhang & Jie Chen & Gang Li, 2019. "Energy modelling and energy saving strategy analysis of a machine tool during non-cutting status," International Journal of Production Research, Taylor & Francis Journals, vol. 57(14), pages 4451-4467, July.
  • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:14:p:4451-4467
    DOI: 10.1080/00207543.2018.1436787
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    Cited by:

    1. Nailiang Li & Caihong Feng, 2021. "Research on Machining Workshop Batch Scheduling Incorporating the Completion Time and Non-Processing Energy Consumption Considering Product Structure," Energies, MDPI, vol. 14(19), pages 1-26, September.
    2. Zhang, Yuanhui & Cai, Wei & He, Yan & Peng, Tao & Jia, Shun & Lai, Kee-hung & Li, Li, 2022. "Forward-and-reverse multidirectional turning: A novel material removal approach for improving energy efficiency, processing efficiency and quality," Energy, Elsevier, vol. 260(C).
    3. Bin Chen & Wenying Xie & Fuyou Huang & Xinyang Li, 2020. "Energy-saving and pricing decisions in a sustainable supply chain considering behavioral concerns," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-20, August.

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