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Establishing prediction models for feeding power and material drilling power to support sustainable machining

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Abstract

Analyzing and modeling the power and energy consumption of machining processes have been considered to be an important approach to enhance the energy performance of manufacturing process. As drilling process is one of the most commonly used technologies in manufacturing, specialized research on the modeling method of material drilling power of drilling is very rare. The predictive accuracies of existing models for feeding power and material drilling power have great potential for improving. To fill this gap, improved predictive models of feeding power and material drilling power of drilling process are proposed in this research. To show the feasibility of the proposed modeling methods of feeding power and material drilling power, extensive experimental studies were carried out on a machining center. The research results show that the predictive accuracies are generally improved by 1–2% with the established feeding power model in this article compared with the existing model. Moreover, the predictive accuracies of all the tested drilling cases by using the established material drilling power model are above 90.0%, and a high average accuracy of 95.0% is achieved. The established power models of drilling process in this research build a good basis for modeling of total drilling power and energy optimization.

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Acknowledgements

The authors gratefully thank all the editors and reviewers for their valuable suggestions on the improvement of this paper.

Funding

This research is supported by the National Natural Science Foundation of China (Grant No. 71701113), the Project of Shandong Province Higher Educational Science and Technology Program (Grant No. J17KA167), the Shandong Provincial Natural Science Foundation, China (Grant No. ZR2016GQ11), and by the SDUST Research Fund (Grant No. 2018YQJH103).

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Shun Jia & Qinghe Yuan

  2. State Key Laboratory of Mining Disaster Prevention and Control Co-founded by Shandong Province and the Ministry of Science and Technology, Shandong University of Science and Technology, Qingdao, 266590, China

    Shun Jia & Qinghe Yuan

  3. College of Engineering and Technology, Southwest University, Chongqing, 400715, China

    Wei Cai

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

    Jingxiang Lv

  5. State Key Laboratory of Fluid Power and Mechatronic Systems, School of Mechanical Engineering, Zhejiang University, Hangzhou, 310027, China

    Luoke Hu

Authors
  1. Shun Jia
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  2. Qinghe Yuan
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  3. Wei Cai
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  4. Jingxiang Lv
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  5. Luoke Hu
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Corresponding authors

Correspondence to Shun Jia or Qinghe Yuan.

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Jia, S., Yuan, Q., Cai, W. et al. Establishing prediction models for feeding power and material drilling power to support sustainable machining. Int J Adv Manuf Technol 100, 2243–2253 (2019). https://doi.org/10.1007/s00170-018-2861-5

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  • DOI: https://doi.org/10.1007/s00170-018-2861-5

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