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Real-Time Data Assimilation in Welding Operations Using Thermal Imaging and Accelerated High-Fidelity Digital Twinning

Author

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  • Pablo Pereira Álvarez

    (Électricité de France Research & Development and Innovation (EDF R&D), 6 Quai Watier, 78400 Chatou, France
    Centre des Matériaux (CMAT), MINES ParisTech, PSL University, CNRS UMR 7633, BP 87, 91003 Evry, France)

  • Pierre Kerfriden

    (Centre des Matériaux (CMAT), MINES ParisTech, PSL University, CNRS UMR 7633, BP 87, 91003 Evry, France
    School of Engineering, Cardiff University, Queen’s Buildings, The Parade, Cardiff CF24 3AA, UK)

  • David Ryckelynck

    (Centre des Matériaux (CMAT), MINES ParisTech, PSL University, CNRS UMR 7633, BP 87, 91003 Evry, France)

  • Vincent Robin

    (Électricité de France Research & Development and Innovation (EDF R&D), 6 Quai Watier, 78400 Chatou, France)

Abstract

Welding operations may be subjected to different types of defects when the process is not properly controlled and most defect detection is done a posteriori. The mechanical variables that are at the origin of these imperfections are often not observable in situ. We propose an offline/online data assimilation approach that allows for joint parameter and state estimations based on local probabilistic surrogate models and thermal imaging in real-time. Offline, the surrogate models are built from a high-fidelity thermomechanical Finite Element parametric study of the weld. The online estimations are obtained by conditioning the local models by the observed temperature and known operational parameters, thus fusing high-fidelity simulation data and experimental measurements.

Suggested Citation

  • Pablo Pereira Álvarez & Pierre Kerfriden & David Ryckelynck & Vincent Robin, 2021. "Real-Time Data Assimilation in Welding Operations Using Thermal Imaging and Accelerated High-Fidelity Digital Twinning," Mathematics, MDPI, vol. 9(18), pages 1-25, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2263-:d:635910
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    References listed on IDEAS

    as
    1. Michael E. Tipping & Christopher M. Bishop, 1999. "Probabilistic Principal Component Analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 611-622.
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