[Modèles cinématiques améliorés par les données et guidés par la mécanique pour la corrélation d’images volumiques résolue en temps]
X-ray Computed Tomography (XCT) combined with Digital Volume Correlation (DVC) enables for internal displacement and strain measurements in deforming materials. The long acquisition time of tomographic scans restricts analyses to a few static loading steps and prevents from time-dependent or nonlinear mechanism quantification. Projection-based DVC (P-DVC) addresses this limitation by exploiting projections acquired during continuous loading, providing substantially higher temporal sampling.
This study assesses a spacetime framework of projection-enhanced DVC for the in situ investigation of a 3D-printed lattice. Global DVC displacement fields are used to construct reduced spatial bases, namely, (i) a pure data-driven basis, (ii) a mechanics-only basis derived from an elastic compression solution, and (iii) a hybrid mechanics-data basis combining both. P-DVC then exploits the projections to identify the temporal amplitudes associated with these modes, thereby reconstructing the time-resolved kinematics with a resolution of 3.7 s, nearly two orders of magnitude faster than conventional 3D-to-3D DVC.
Nonlinear mechanisms were detected ahead of their clear expression in tomographic reconstructions. The hybrid reduced-order approach provided interpretable kinematic fields while preserving the predictive accuracy of data-driven strategies. The projection-enhanced DVC framework thus enabled for temporally dense and spatially resolved in situ measurements, providing the type of rich datasets required for the identification and learning of complex constitutive models.
La tomographie par rayons X (XCT) combinée à la corrélation d’images volumiques (DVC) permet la mesure de champs de déplacements et des déformations internes dans les matériaux sous chargement. Le temps d’acquisition long des scans tomographiques limite les analyses à quelques pas de chargement statique et empêche la quantification des mécanismes dépendant du temps ou non linéaires. La DVC basée sur les projections (P-DVC) pallie cette limitation en exploitant les projections acquises lors d’un chargement continu, offrant un échantillonnage temporel nettement supérieur.
Cette étude évalue un cadre spatio-temporel de DVC augmentée par les projections pour l’étude in situ d’un métamatériau imprimé en 3D. Les champs de déplacement mesurés par la DVC sont utilisés pour construire des bases spatiales réduites, à savoir : (i) une base purement basée sur les données, (ii) une base purement mécanique dérivée d’une solution de compression élastique, et (iii) une base hybride mécanique-données combinant les deux. La P-DVC exploite ensuite les projections pour mesurer les amplitudes temporelles associées à ces modes, reconstruisant ainsi la cinématique temporelle avec une résolution de 3,7 s, soit près de deux ordres de grandeur plus rapide que la DVC conventionnelle.
Des mécanismes non linéaires ont été détectés avant même leur expression claire dans les reconstructions tomographiques. L’approche hybride d’ordre réduit fournit des champs cinématiques interprétables tout en préservant la cohérence prédictive des stratégies basées sur les données. Le cadre de la DVC augmentée par les projections a ainsi permis des mesures in situ temporellement denses et spatialement résolues, fournissant des données riches nécessaires à l’identification et à l’apprentissage de modèles de comportement complexes.
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Mots-clés : Tests in situ, corrélation d’images volumiques (DVC), DVC basée sur les projections (P-DVC), analyses spatio-temporelles, réduction de modèles
Pierre Latil 1 ; Malo Valmalle 1 ; Benjamin Smaniotto 1 ; Clément Jailin 1 ; François Hild 1
CC-BY 4.0
Pierre Latil; Malo Valmalle; Benjamin Smaniotto; Clément Jailin; François Hild. Mechanics-guided data-enhanced kinematic models for time-resolved DVC. Comptes Rendus. Mécanique, Volume 354 (2026), pp. 495-525. doi: 10.5802/crmeca.370
@article{CRMECA_2026__354_G1_495_0,
author = {Pierre Latil and Malo Valmalle and Benjamin Smaniotto and Cl\'ement Jailin and Fran\c{c}ois Hild},
title = {Mechanics-guided data-enhanced kinematic models for time-resolved {DVC}},
journal = {Comptes Rendus. M\'ecanique},
pages = {495--525},
year = {2026},
publisher = {Acad\'emie des sciences, Paris},
volume = {354},
doi = {10.5802/crmeca.370},
language = {en},
}
TY - JOUR AU - Pierre Latil AU - Malo Valmalle AU - Benjamin Smaniotto AU - Clément Jailin AU - François Hild TI - Mechanics-guided data-enhanced kinematic models for time-resolved DVC JO - Comptes Rendus. Mécanique PY - 2026 SP - 495 EP - 525 VL - 354 PB - Académie des sciences, Paris DO - 10.5802/crmeca.370 LA - en ID - CRMECA_2026__354_G1_495_0 ER -
%0 Journal Article %A Pierre Latil %A Malo Valmalle %A Benjamin Smaniotto %A Clément Jailin %A François Hild %T Mechanics-guided data-enhanced kinematic models for time-resolved DVC %J Comptes Rendus. Mécanique %D 2026 %P 495-525 %V 354 %I Académie des sciences, Paris %R 10.5802/crmeca.370 %G en %F CRMECA_2026__354_G1_495_0
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