Issue |
Mechanics & Industry
Volume 24, 2023
|
|
---|---|---|
Article Number | 34 | |
Number of page(s) | 21 | |
DOI | https://doi.org/10.1051/meca/2023029 | |
Published online | 15 September 2023 |
Research Article
A neural network-based data-driven local modeling of spotwelded plates under impact
1
Université Paris-Saclay, CentraleSupélec, ENS Paris-Saclay, CNRS, LMPS – Laboratoire de Mécanique Paris-Saclay,
91190
Gif-sur-Yvette, France
2
EPF School of Engineering,
94230
Cachan, France
3
IUF, Institut Universitaire de France,
Paris, France
4
Altair Engineering France,
92160
Antony, France
5
Stellantis,
78140
Velizy-Villacoublay, France
* e-mail: afsal.pulikkathodi@ens-paris-saclay.fr
Received:
28
February
2023
Accepted:
27
July
2023
Solving large structural problems with multiple complex localized behaviors is extremely challenging. To address this difficulty, both intrusive and non-intrusive Domain Decomposition Methods (DDM) have been developed in the past, where the refined model (local) is solved separately in its own space and time scales. In this work, the Finite Element Method (FEM) at the local scale is replaced with a data-driven Reduced Order Model (ROM) to further decrease computational time. The reduced model aims to create a low-cost, accurate and efficient mapping from interface velocities to interface forces and enable the prediction of their time evolution. The present work proposes a modeling technique based on the Physics-Guided Architecture of Neural Networks (PGANNs), which incorporates physical variables other than input/output variables into the neural network architecture. We develop this approach on a 2D plate with a hole as well as a 3D case with spot-welded plates undergoing fast deformation, representing nonlinear elastoplasticity problems. Neural networks are trained using simulation data generated by explicit dynamic FEM solvers. The PGANN results are in good agreement with the FEM solutions for both test cases, including those in the training dataset as well as the unseen dataset, given the loading type is present in the training set.
Key words: Artificial neural networks / data-driven modelling / local / global coupling / explicit dynamics / physics-guided architecture
© A. Pulikkathodi et al., Published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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