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A mechanistic model of energy consumption in milling

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  • Reza Imani Asrai
  • Stephen T. Newman
  • Aydin Nassehi

Abstract

In this paper, a novel mechanistic model is proposed and validated for the consumption of energy in milling processes. The milling machine is considered as a thermodynamic system. Mechanisms of the significant energy conversion processes within the system are used to construct an explicit expression for the power consumption of the machine as a function of the cutting parameters. This model has been validated experimentally and is shown to be significantly more accurate than popular existing models. A simplified form of the model is also proposed that provides a balance between complexity and accuracy. The novelty of the model is that it maps the flow of energy within a machine tool, based solely on the active mechanisms of energy conversion. As a result, only limited assumptions are made in the model, resulting in an error of less than one per cent, verified by experiments. This accurate model can be used to substantially reduce energy consumption in milling processes at machine and factory levels leading to massive cost savings and reduction of environmental impact of numerous industries. The generality of the modelling method makes it applicable to other types of machine tools with minimal adjustments.

Suggested Citation

  • Reza Imani Asrai & Stephen T. Newman & Aydin Nassehi, 2018. "A mechanistic model of energy consumption in milling," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 642-659, January.
    • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:1-2:p:642-659
    DOI: 10.1080/00207543.2017.1404160
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    Cited by:

    1. He, Yan & Wu, Pengcheng & Li, Yufeng & Wang, Yulin & Tao, Fei & Wang, Yan, 2020. "A generic energy prediction model of machine tools using deep learning algorithms," Applied Energy, Elsevier, vol. 275(C).
    2. Gert van Wyk & Vinessa Naidoo & E. Innocents Edoun, 2021. "Guiding Principles for Establishing Energy Consumption Reduction and Increase Production Performance in Manufacturing," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 502-515.
    3. 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).
    4. 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.
    5. Dietrich, Bastian & Walther, Jessica & Weigold, Matthias & Abele, Eberhard, 2020. "Machine learning based very short term load forecasting of machine tools," Applied Energy, Elsevier, vol. 276(C).

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