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Energy modeling and visualization analysis method of drilling processes in the manufacturing industry

Author

Listed:
  • Jia, Shun
  • Cai, Wei
  • Liu, Conghu
  • Zhang, Zhongwei
  • Bai, Shuowei
  • Wang, Qiuyan
  • Li, Shuoshuo
  • Hu, Luoke

Abstract

Energy modeling and visualization of machining have been recognized as effective and powerful ways to explore energy-saving potential and to improve energy efficiency. However, energy modeling and visualization of the drilling process have not been investigated adequately. To address this challenge, sub-power models-based energy modeling and multi-angle energy visualization analysis methods of drilling process were proposed in this study. More specifically, three tasks were carried out: (1) detailed sub-power models of drilling were established; (2) sub-power models-based energy modeling method of drilling was proposed; (3) based on the detailed sub-power models and energy data, multi-angle energy visualization analysis was conducted. Application of the proposed drilling energy model in common drilling processes indicated that its average prediction accuracy of the proposed drilling energy model was 96.2%. The results also showed that 7417.8 J energy saving and 12.6% energy efficiency improvement were achieved with the visualization analysis. The proposed method contributed to energy-saving activities for the drilling process, including providing high accuracy energy model, analyzing energy saving potential and improving energy efficiency. We believe that the outcomes of this research can help engineers and managers to better understand and manage the energy characteristics of drilling.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:228:y:2021:i:c:s0360544221008161
    DOI: 10.1016/j.energy.2021.120567
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    References listed on IDEAS

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    1. Xiao, Qinge & Li, Congbo & Tang, Ying & Li, Lingling & Li, Li, 2019. "A knowledge-driven method of adaptively optimizing process parameters for energy efficient turning," Energy, Elsevier, vol. 166(C), pages 142-156.
    2. 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.
    3. Schudeleit, Timo & Züst, Simon & Weiss, Lukas & Wegener, Konrad, 2016. "The Total Energy Efficiency Index for machine tools," Energy, Elsevier, vol. 102(C), pages 682-693.
    4. Li, Yufeng & He, Yan & Wang, Yan & Wang, Yulin & Yan, Ping & Lin, Shenlong, 2015. "A modeling method for hybrid energy behaviors in flexible machining systems," Energy, Elsevier, vol. 86(C), pages 164-174.
    5. 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.
    6. Schudeleit, Timo & Züst, Simon & Wegener, Konrad, 2015. "Methods for evaluation of energy efficiency of machine tools," Energy, Elsevier, vol. 93(P2), pages 1964-1970.
    7. Salahi, Niloofar & Jafari, Mohsen A., 2016. "Energy-Performance as a driver for optimal production planning," Applied Energy, Elsevier, vol. 174(C), pages 88-100.
    8. Reza Imani Asrai & Stephen T. Newman & Aydin Nassehi, 2018. "A mechanistic model of energy consumption in milling," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 642-659, January.
    9. 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.
    10. 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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    Cited by:

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    2. Tatiana N. Ivanova & Witold Biały & Aleksander I. Korshunov & Jerzy Jura & Krzysztof Kaczmarczyk & Krzysztof Turczyński, 2022. "Increasing Energy Efficiency in Well Drilling," Energies, MDPI, vol. 15(5), pages 1-16, March.
    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. Shuai Wang & Jizhuang Hui & Bin Zhu & Ying Liu, 2022. "Adaptive Genetic Algorithm Based on Fuzzy Reasoning for the Multilevel Capacitated Lot-Sizing Problem with Energy Consumption in Synchronizer Production," Sustainability, MDPI, vol. 14(9), pages 1-24, April.
    5. Wang, Jinling & Tian, Yebing & Hu, Xintao & Han, Jinguo & Liu, Bing, 2023. "Integrated assessment and optimization of dual environment and production drivers in grinding," Energy, Elsevier, vol. 272(C).
    6. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).

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