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Energy consumption analysis on sheet metal forming: focusing on the deep drawing processes

  • Mengdi Gao
  • Haihong Huang
  • Qingyang Wang
  • Zhifeng Liu
  • Xinyu Li
ORIGINAL ARTICLE

Abstract

Nowadays, increased concerns about energy-efficient and environmentally benign manufacturing process have become an emerging issue. Deep drawing is a widely used traditional process especially in the automotive and aeronautical industries, which is also energy intensive and accounts for a large portion of energy consumption in metal forming sectors. The investigation of sheet metal forming energy saving is worth studying and, within this research field, a certain lack of energy prediction models or tools is still present. The aim of this research was to contribute towards the development of analytical models and logic for estimating the energy consumption in sheet metal forming processes, which could provide the basic for a significant energy reduction. An application to a spherical part deep drawing process was used to verify the validity and rationality of the models with the results deviation within 10%. And the effects of the process parameters on energy consumption were discussed. The overall contribution of the main process parameters on energy consumption was analyzed through a matrix of experiments. The obtained results allowed to be used for guiding process optimization of energy saving and the selection of manufacturing parameters.

Keywords

Deep drawing Energy prediction models Sheet metal forming Energy saving 

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Notes

Acknowledgements

This work is financially supported by Funds for International Cooperation and Exchange of the National Natural Science Foundation of China (51561125002), the National Natural Science Foundation of China (51722502), the University Scientific Research key project in Anhui Province of China (KJ2017A438), and the 61st instalment postdoctoral Science Foundation of China (2017M611574).

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Mengdi Gao
    • 1
  • Haihong Huang
    • 2
  • Qingyang Wang
    • 1
  • Zhifeng Liu
    • 2
  • Xinyu Li
    • 2
  1. 1.School of Mechanical and Electronic EngineeringSuzhou UniversitySuzhouChina
  2. 2.School of Mechanical EngineeringHefei University of TechnologyHefeiChina

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