4.7 Article

Proper Generalized Decomposition stereocorrelation to measure kinematic fields for high speed impact on laminated glass

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ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2023.116217

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Digital Image Correlation (DIC); High-speed camera; Impact test; Laminated glass; Proper Generalized Decomposition (PGD); Space-time regularization

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Stereocorrelation (SC) is a powerful tool for measuring 3D surface deformations in mechanical tests, but it may face convergence issues for large displacements or speckle changes. This study proposes a global space-time formulation using Proper Generalized Decomposition (PGD) as a regularization strategy. The entire image sequence is treated as a whole, and the kinematics is sought as the sum of modes, each being a function of space multiplied by a function of time. A discriminative benchmark case of an impact test on glass laminated with PVB layers is chosen to validate this approach, and the results demonstrate that the new formulation outperforms classical approaches in terms of robustness, measurement accuracy, and processing time.
Stereocorrelation (SC) is a powerful tool for measuring 3D surface deformations in mechanical tests. However, it may suffer from convergence issues for large displacements or speckle changes. A global space-time formulation is proposed herein using Proper Generalized Decomposition (PGD) as a regularization strategy. The entire image sequence is considered as a whole, and the kinematics is sought as the sum of modes, each of which is a function of space multiplied by a function of time (smoothed over high frequencies if needed). A discriminative benchmark case is chosen to validate this approach: an impact test on glass laminated with PVB layers. This case is difficult for many reasons, particularly because of severe contrast changes due to light reflections occurring at several places. It is shown that the new formulation outperforms classical approaches in robustness and measurement accuracy and provides results within a much shorter processing time. & COPY; 2023 Elsevier B.V. All rights reserved.

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