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Establishment of an Improved Material-Drilling Power Model to Support Energy Management of Drilling Processes

Author

Listed:
  • Shun Jia

    (Department of Finance and Economics, Shandong University of Science and Technology, Jinan 250031, China
    Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Qingwen Yuan

    (Department of Finance and Economics, Shandong University of Science and Technology, Jinan 250031, China)

  • Wei Cai

    (College of Engineering and Technology, Southwest University, Chongqing 400715, China)

  • Qinghe Yuan

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

  • Conghu Liu

    (School of Mechanical and Electronic Engineering, Suzhou University, Suzhou 234000, China)

  • Jingxiang Lv

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

  • Zhongwei Zhang

    (Department of Mechanical Manufacturing and Automation, Henan University of Technology, Zhengzhou 450001, China)

Abstract

Drilling processes, as some of the most widely used machining processes in the manufacturing industry, play an important role in manufacturing process energy-saving programs. However, research focus on energy modeling of drilling processes, especially for the modeling of material-drilling power, are really scarce. To bridge this gap, an improved material-drilling power model is proposed in this paper. The obtained material-drilling power model can improve the accuracy of the material-drilling power and lay a good foundation for energy modeling and optimization of drilling processes. Finally, experimental studies were carried out on an XHK-714F CNC machining center (Hangzhou HangJi Machine Tool Co., Ltd., Hangzhou, China) and a JTVM6540 CNC milling machine (Jinan Third Machine Tool Co., Ltd., Jinan, China). The results showed that predictive accuracies with the proposed model are generally higher than 96% for all the test cases.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2013-:d:161568
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    References listed on IDEAS

    as
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    Cited by:

    1. 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.
    2. 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.
    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).

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