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Prediction of cross-sectional features of SPR joints based on the punch force-displacement curve using machine learning

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Abstract

Self-piercing riveting has gained relevance in the automotive industry as an alternative method to resistance spot welding for the joining of two or more metal sheets. Its simulation via the finite element method, which includes large deformation, plasticity, and fracture mechanics phenomena, has been previously studied in depth. However, costly experimental tests are still required for the characterization of several uncertain variables such as the mechanical properties of all materials, or the friction coefficients of all existing contact pairs. This paper proposes a machine learning model, which can be trained with both experimental and numerical data, in order to predict the cross-sectional features of the riveted joint from the corresponding punch force-displacement curves, such as the interlock distance or the minimum thickness of the lower sheet. In order to achieve this goal, a parametric study has been first carried out by means of a finite element model, by varying the mechanical properties of the upper and lower sheet materials as well as the friction coefficients, while keeping constant the rivet material and all geometries involved. All the obtained force-displacement curves, defined by a large number of points, have been initially projected to a lower-dimensional space via a convolutional autoencoder. Then, a multilayer perceptron has been used to associate their latent space to their corresponding final geometric features. It was found that there is a strong correlation between the force-displacement curves and the final geometric features, allowing for further studies including variation in the geometry or in the rivet material.

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Funding

This work was conducted with the help of the French Technological Research Institute for Materials, Metallurgy and Processes (IRT-M2P). The authors would like to acknowledge IRT-M2P and the partners of the project RESEM4 led by IRT-M2P

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Authors and Affiliations

  1. PIMM Laboratory, Arts et Métiers Institute of Technology, 151 boulevard de l’Hôpital, 75013, Paris, France

    Borja Ferrándiz & Francisco Chinesta

  2. French Technological Research Institute for Materials, Metallurgy and Processes (IRT- M2P), 4 rue Augustin Fresnel, 57070, Metz, France

    Borja Ferrándiz & Nicolas Kohout

  3. Department of Mechanical and Mechatronics Engineering, College of Engineering, Dhofar University, Salalah, 211, Oman

    Monzer Daoud

Authors
  1. Borja Ferrándiz
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  2. Monzer Daoud
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  3. Nicolas Kohout
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  4. Francisco Chinesta
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Contributions

All authors contributed to the study conception and design. Simulations, data collection and analysis were performed by B. Ferrándiz and M. Daoud. The first draft of the manuscript was written by B. Ferrándiz and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript

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Correspondence to Borja Ferrándiz.

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Ferrándiz, B., Daoud, M., Kohout, N. et al. Prediction of cross-sectional features of SPR joints based on the punch force-displacement curve using machine learning. Int J Adv Manuf Technol 128, 4023–4034 (2023). https://doi.org/10.1007/s00170-023-12102-9

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  • DOI: https://doi.org/10.1007/s00170-023-12102-9

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