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

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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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    Cited by:

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    2. 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).
    3. Zhiqiang Yan & Jian Huang & Jingxiang Lv & Jizhuang Hui & Ying Liu & Hao Zhang & Enhuai Yin & Qingtao Liu, 2022. "A New Method of Predicting the Energy Consumption of Additive Manufacturing considering the Component Working State," Sustainability, MDPI, vol. 14(7), pages 1-23, March.
    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.
    5. 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.
    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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