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Energy Demand Modeling Methodology of Key State Transitions of Turning Processes

Author

Listed:
  • Shun Jia

    (Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Qinghe Yuan

    (Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Dawei Ren

    (Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Jingxiang Lv

    (Key Laboratory of Contemporary Design and Integrated Manufacturing Technology, Ministry of Education, Northwestern Polytechnical University, Xi’an 710072, China)

Abstract

Energy demand modeling of machining processes is the foundation of energy optimization. Energy demand of machining state transition is integral to the energy requirements of the machining process. However, research focus on energy modeling of state transition is scarce. To fill this gap, an energy demand modeling methodology of key state transitions of the turning process is proposed. The establishment of an energy demand model of state transition could improve the accuracy of the energy model of the machining process, which also provides an accurate model and reliable data for energy optimization of the machining process. Finally, case studies were conducted on a CK6153 i CNC lathe, the results demonstrating that predictive accuracy with the proposed method is generally above 90% for the state transition cases.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:462-:d:94796
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. Lijun Song & Jing Shi & Anda Pan & Jie Yang & Jun Xie, 2020. "A Dynamic Multi-Swarm Particle Swarm Optimizer for Multi-Objective Optimization of Machining Operations Considering Efficiency and Energy Consumption," Energies, MDPI, vol. 13(10), pages 1-18, May.
    4. Shun Jia & Shang Wang & Jingxiang Lv & Wei Cai & Na Zhang & Zhongwei Zhang & Shuowei Bai, 2021. "Multi-Objective Optimization of CNC Turning Process Parameters Considering Transient-Steady State Energy Consumption," Sustainability, MDPI, vol. 13(24), pages 1-23, December.

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