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Automatic and robust design for multiple self-piercing riveted joints using deep neural network

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Abstract

Self-piercing riveting (SPR) is one of the major joining processes in the automotive industry. However, the frequently used design method for multiple SPR joints is still time-consuming, and relies heavily on engineers’ experience and experimental SPR tests. This research focuses on simplifying the joint design process and improving the design robustness for multiple SPR joints. A fast joint quality prediction model was firstly developed using the deep neural network (DNN), and verified through experimental SPR tests. Then, a joint robustness evaluation strategy was proposed by combining the developed DNN with the Monte Carlo method to consider the manufacturing tolerances of rivet, sheets, and die. Afterwards, two novel approaches were developed to quickly identify the suitable joint design with minimum rivet/die combinations for multiple sheet combinations. The first approach realizes automatic design of robust joints with the DNN model, the robustness evaluation method, and a proposed automatic selection algorithm. The second approach was developed based on application range maps of potential rivet/die combinations, and can be used to quickly identify the suitable joint design for multiple sheet combinations. Finally, experimental SPR tests were conducted to validate the effectiveness of the two joint design methods. Results from this study are helpful to simplify the design process, shorten the design cycle, and improve the robustness of multiple SPR joints.

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Acknowledgements

The authors would like to thank Dr. Matthias Wissling, Paul Bartig, and their team members from Tucker GmbH for their supports during the laboratory tests.

Funding

This research is funded by Jaguar Land Rover.

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Authors and Affiliations

  1. School of Engineering, University of Warwick, Coventry, CV4 7AL, UK

    Huan Zhao, Yunpeng Liu & Xianping Liu

  2. Hansher Consulting Ltd, Coventry, UK

    Li Han

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  3. Yunpeng Liu
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  4. Xianping Liu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Huan Zhao and Yunpeng Liu. The first draft of the manuscript was written by Huan Zhao, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Xianping Liu.

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Zhao, H., Han, L., Liu, Y. et al. Automatic and robust design for multiple self-piercing riveted joints using deep neural network. Int J Adv Manuf Technol 122, 947–975 (2022). https://doi.org/10.1007/s00170-022-09893-8

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  • DOI: https://doi.org/10.1007/s00170-022-09893-8

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