IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i20p7064-d1258544.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/20/7064/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/20/7064/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. Salahi, Niloofar & Jafari, Mohsen A., 2016. "Energy-Performance as a driver for optimal production planning," Applied Energy, Elsevier, vol. 174(C), pages 88-100.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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).
    3. 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.
    4. Zhang, Jiaqi & Han, Xin & Li, Li & Jia, Shun & Jiang, Zhigang & Duan, Xiangmin & Lai, Kee-hung & Cai, Wei, 2023. "Multi-objective optimisation for energy saving and high efficiency production oriented multidirectional turning based on improved fireworks algorithm considering energy, efficiency and quality," Energy, Elsevier, vol. 284(C).
    5. Liu, Conghu & Cai, Wei & Dinolov, Ognyan & Zhang, Cuixia & Rao, Weizhen & Jia, Shun & Li, Li & Chan, Felix T.S., 2018. "Emergy based sustainability evaluation of remanufacturing machining systems," Energy, Elsevier, vol. 150(C), pages 670-680.
    6. Wen, Xuanhao & Cao, Huajun & Hon, Bernard & Chen, Erheng & Li, Hongcheng, 2021. "Energy value mapping: A novel lean method to integrate energy efficiency into production management," Energy, Elsevier, vol. 217(C).
    7. Cai, Wei & Liu, Fei & Dinolov, Ognyan & Xie, Jun & Liu, Peiji & Tuo, Junbo, 2018. "Energy benchmarking rules in machining systems," Energy, Elsevier, vol. 142(C), pages 258-263.
    8. 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.
    9. Catanzaro, Daniele & Pesenti, Raffaele & Ronco, Roberto, 2023. "Job scheduling under Time-of-Use energy tariffs for sustainable manufacturing: a survey," European Journal of Operational Research, Elsevier, vol. 308(3), pages 1091-1109.
    10. Ma, Shuaiyin & Ding, Wei & Liu, Yang & Ren, Shan & Yang, Haidong, 2022. "Digital twin and big data-driven sustainable smart manufacturing based on information management systems for energy-intensive industries," Applied Energy, Elsevier, vol. 326(C).
    11. Hu, Luoke & Peng, Chen & Evans, Steve & Peng, Tao & Liu, Ying & Tang, Renzhong & Tiwari, Ashutosh, 2017. "Minimising the machining energy consumption of a machine tool by sequencing the features of a part," Energy, Elsevier, vol. 121(C), pages 292-305.
    12. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    13. Pi, Dawei & Xue, Pengyu & Wang, Weihua & Xie, Boyuan & Wang, Hongliang & Wang, Xianhui & Yin, Guodong, 2023. "Automotive platoon energy-saving: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    14. Murat Gunduz & Ayman Fahmi Naser, 2017. "Cost Based Value Stream Mapping as a Sustainable Construction Tool for Underground Pipeline Construction Projects," Sustainability, MDPI, vol. 9(12), pages 1-20, November.
    15. Perroni, Marcos G. & Gouvea da Costa, Sergio E. & Pinheiro de Lima, Edson & Vieira da Silva, Wesley & Tortato, Ubiratã, 2018. "Measuring energy performance: A process based approach," Applied Energy, Elsevier, vol. 222(C), pages 540-553.
    16. 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.
    17. Kiki Ayu & Akilu Yunusa-Kaltungo, 2020. "A Holistic Framework for Supporting Maintenance and Asset Management Life Cycle Decisions for Power Systems," Energies, MDPI, vol. 13(8), pages 1-32, April.
    18. 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.
    19. Xia, Tangbin & Si, Guojin & Shi, Guo & Zhang, Kaigan & Xi, Lifeng, 2022. "Optimal selective maintenance scheduling for series–parallel systems based on energy efficiency optimization," Applied Energy, Elsevier, vol. 314(C).
    20. Cai, Wei & Liu, Fei & Zhang, Hua & Liu, Peiji & Tuo, Junbo, 2017. "Development of dynamic energy benchmark for mass production in machining systems for energy management and energy-efficiency improvement," Applied Energy, Elsevier, vol. 202(C), pages 715-725.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:20:p:7064-:d:1258544. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.