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Article Dans Une Revue Mathematics Année : 2021

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

Pablo Pereira Álvarez
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Pierre Kerfriden
David Ryckelynck
Vincent Robin
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Résumé

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY 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.
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Dates et versions

hal-03345602 , version 1 (15-09-2021)

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Pablo Pereira Álvarez, Pierre Kerfriden, David Ryckelynck, Vincent Robin. Real-Time Data Assimilation in Welding Operations Using Thermal Imaging and Accelerated High-Fidelity Digital Twinning. Mathematics , 2021, 9 (18), pp.2263. ⟨10.3390/math9182263⟩. ⟨hal-03345602⟩
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