Real-time data assimilation in welding operations using thermal imaging and accelerated high-fidelity digital twinning

Pereira Álvarez, Pablo, Kerfriden, Pierre ORCID: https://orcid.org/0000-0002-7749-3996, Ryckelynck, David and Robin, Vincent 2021. Real-time data assimilation in welding operations using thermal imaging and accelerated high-fidelity digital twinning. Mathematics 9 (18) , 2263. 10.3390/math9182263

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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.

Item Type:	Article
Date Type:	Published Online
Status:	Published
Schools:	Advanced Research Computing @ Cardiff (ARCCA) Engineering
Additional Information:	This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Publisher:	MDPI
ISSN:	2227-7390
Date of First Compliant Deposit:	6 October 2021
Date of Acceptance:	7 September 2021
Last Modified:	01 Aug 2024 13:37
URI:	https://orca.cardiff.ac.uk/id/eprint/144627

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