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

Pablo Pereira Álvarez, Pierre Kerfriden, David Ryckelynck and Vincent Robin
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Pablo Pereira Álvarez: Électricité de France Research & Development and Innovation (EDF R&D), 6 Quai Watier, 78400 Chatou, France
Pierre Kerfriden: Centre des Matériaux (CMAT), MINES ParisTech, PSL University, CNRS UMR 7633, BP 87, 91003 Evry, France
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

Mathematics, 2021, vol. 9, issue 18, 1-25

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.

Keywords: data assimilation; model order reduction; finite elements analysis; high dimensional data; welding (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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