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An Energy Consumption Estimation Method for the Tool Setting Process in CNC Milling Based on the Modular Arrangement of Predetermined Time Standards

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  • Zhaohui Feng

    (School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China
    Green Manufacturing Engineering Research Institute, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Xinru Ding

    (School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China)

  • Hua Zhang

    (Green Manufacturing Engineering Research Institute, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Ying Liu

    (Department of Mechanical Engineering, School of Engineering, Cardiff University, Cardiff CF24 3AA, UK)

  • Wei Yan

    (School of Automobile and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China
    Green Manufacturing Engineering Research Institute, Wuhan University of Science and Technology, Wuhan 430081, China)

  • Xiaoli Jiang

    (State Nuclear Zhanjiang Nuclear Power Company Ltd., Zhanjiang 524000, China)

Abstract

Modeling and estimating the energy consumption of computer numerical control (CNC) milling systems have been recognized as essential ways to realize lean energy consumption management and improve energy efficiency performance. As the preparatory phase, considerable time and energy are consumed in the tool setting process. However, research on the tool setting process mainly focuses on accuracy and operational efficiency, and the energy consumption is usually ignored or simplified. Accurately estimating the energy consumption of the tool setting process is thus indispensable for reducing the energy consumption of CNC milling systems and improving their energy efficiency. To bridge this gap, an energy consumption estimation method for the tool setting process in CNC milling based on the modular arrangement of predetermined time standards (MODAPTS) is presented. It includes three steps: (i) operations decomposition and determination of the MODAPTS codes for the tool setting process, (ii) power modeling of the basic action elements of the machine tool, and (iii) energy consumption modeling of the tool setting process. Finally, a case study was conducted to illustrate the practicability of the proposed method via energy consumption modeling of the tool setting process using an XH714D CNC machine center with a square workpiece, in which the estimation values of the operating time and the energy consumption for the tool setting process were 210.786 s and 140,681.68 J, respectively. The proposed method can increase the transparency of energy consumption and help establish labor-hour quotas and energy consumption allowances in the tool setting process.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7064-:d:1258544
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    References listed on IDEAS

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    1. Jianhua Cao & Xuhui Xia & Lei Wang & Zelin Zhang & Xiang Liu, 2021. "A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network," Sustainability, MDPI, vol. 13(24), pages 1-16, December.
    2. Giampieri, A. & Ling-Chin, J. & Ma, Z. & Smallbone, A. & Roskilly, A.P., 2020. "A review of the current automotive manufacturing practice from an energy perspective," Applied Energy, Elsevier, vol. 261(C).
    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. 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).
    5. Salahi, Niloofar & Jafari, Mohsen A., 2016. "Energy-Performance as a driver for optimal production planning," Applied Energy, Elsevier, vol. 174(C), pages 88-100.
    6. Cai, Wei & Liu, Fei & Zhou, XiaoNa & Xie, Jun, 2016. "Fine energy consumption allowance of workpieces in the mechanical manufacturing industry," Energy, Elsevier, vol. 114(C), pages 623-633.
    7. Shun Jia & Qinghe Yuan & Dawei Ren & Jingxiang Lv, 2017. "Energy Demand Modeling Methodology of Key State Transitions of Turning Processes," Energies, MDPI, vol. 10(4), pages 1-19, April.
    8. Jia, Shun & Yuan, Qinghe & Lv, Jingxiang & Liu, Ying & Ren, Dawei & Zhang, Zhongwei, 2017. "Therblig-embedded value stream mapping method for lean energy machining," Energy, Elsevier, vol. 138(C), pages 1081-1098.
    9. 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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