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Towards less energy intensive heavy-duty machine tools: Power consumption characteristics and energy-saving strategies

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  • Shang, Zhendong
  • Gao, Dong
  • Jiang, Zhipeng
  • Lu, Yong

Abstract

Energy conservation in manufacturing sector has received growing attention in an increasingly more carbon-restrained world due to the great concerns over climate change. The heavy-duty machine tool is usually the critical equipment in a factory, which consumes much more power than normal-sized machine tool but received less attention regarding energy saving. In this research, a generic power consumption model was developed from three hierarchies (i.e. system boundary definition, generic power consumption framework and detailed power consumption), which integrates the design parameters of the machine tool thus enabling the prediction of the power consumption even when the machine tool is physically unavailable. In addition, the relations between the power consumed in air-cutting and cutting states were also discussed, which can significantly simplify the study on machine tool power characteristics. The proposed model was verified by experiments and five power consumption characteristics of the tested heavy-duty machine tools were summarised. Additionally, four strategies for designing an energy-efficient machine tool and four tactics for using an existing machine tool more energy efficiently were proposed. This study represents part of a major comprehensive energy conservation research programme for heavy-duty machine tools, which aims to find solutions to improved energy efficiency for the manufacturing industry.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:178:y:2019:i:c:p:263-276
    DOI: 10.1016/j.energy.2019.04.133
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    References listed on IDEAS

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    1. 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.
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    5. Guo, Yansong & Duflou, Joost R. & Deng, Yelin & Lauwers, Bert, 2018. "A life cycle energy analysis integrated process planning approach to foster the sustainability of discrete part manufacturing," Energy, Elsevier, vol. 153(C), pages 604-617.
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    6. Cai, Wei & Wang, Lianguo & Li, Li & Xie, Jun & Jia, Shun & Zhang, Xugang & Jiang, Zhigang & Lai, Kee-hung, 2022. "A review on methods of energy performance improvement towards sustainable manufacturing from perspectives of energy monitoring, evaluation, optimization and benchmarking," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
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    12. Alexey I. Shinkevich & Tatiana V. Malysheva & Yulia V. Vertakova & Vladimir A. Plotnikov, 2021. "Optimization of Energy Consumption in Chemical Production Based on Descriptive Analytics and Neural Network Modeling," Mathematics, MDPI, vol. 9(4), pages 1-20, February.
    13. Benjie Li & Hualin Zheng & Xiao Yang & Liang Guo & Binglin Li, 2020. "Energy Optimization for Motorized Spindle System of Machine Tools under Minimum Thermal Effects and Maximum Productivity Constraints," Energies, MDPI, vol. 13(22), pages 1-17, November.
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