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SpatioTemporally Adaptive Quadtree Mesh (STAQ) Digital Image Correlation for Resolving Large Deformations Around Complex Geometries and Discontinuities

Yang, J. and Rubino, V. and Ma, Z. and Tao, J. and Yin, Y. and McGhee, A. and Pan, W. and Franck, C. (2022) SpatioTemporally Adaptive Quadtree Mesh (STAQ) Digital Image Correlation for Resolving Large Deformations Around Complex Geometries and Discontinuities. Experimental Mechanics . ISSN 0014-4851. doi:10.1007/s11340-022-00872-4. (In Press) https://resolver.caltech.edu/CaltechAUTHORS:20220725-155836000

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Abstract

Background. Digital image correlation (DIC) is a powerful experimental tool for measuring full-field material deformations. Inherent limitations of typical DIC algorithms can cause a multitude of errors when analyzing the displacement field of samples containing complex geometries or discontinuities. Most adaptations rely on either splitting or augmenting the local DIC subsets that pass through the discontinuity path. However, these methods are challenging to generalize and automate, often requiring significant user intervention. Objective. To address these shortcomings, we present a new, user-friendly automatic experimental approach for resolving the deformation fields around complex geometries and displacement discontinuities, which we call the spatiotemporally adaptive quadtree mesh (STAQ) DIC method. Methods. In this method, the adaptive quadtree mesh is automatically generated from a mask file of the DIC image itself to handle the inherent complex geometry. Subsets that span either geometric or displacement discontinuities are automatically split to improve their DIC accuracy. A binary image mask is also used to inform an interpolation scheme for displacement and strain calculations. Furthermore, we also propose a data-driven reduced order modeling (ROM) approach to further reduce the computational costs by skipping unnecessary image frames thus achieving temporal adaptability for efficiently processing large image sequences. Results. We demonstrate that our STAQ method has high accuracy in solving complex geometric and discontinuous deformation fields in an automated fashion. We find that the proposed data-driven ROM method can provide up to 60% in computational cost savings while maintaining the same level of accuracy compared to a fully processed image set. Conclusions. STAQ DIC is a computationally efficient method for accurately solving geometrically complex and discontinuous deformation fields. Using the data-driven ROM method as part of STAQ can further reduce computational costs for processing large image sequences. An open-source Matlab implementation is freely available.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/s11340-022-00872-4DOIArticle
https://rdcu.be/cSrntPublisherFree ReadCube access
ORCID:
AuthorORCID
Yang, J.0000-0002-5967-980X
Rubino, V.0000-0002-4023-8668
Franck, C.0000-0002-2347-620X
Additional Information:© Society for Experimental Mechanics 2022. Received: 28 September 2021 / Accepted: 27 May 2022. We gratefully acknowledge funding support from the Office of Naval Research under the PANTHER program (Dr. Timothy Bentley; grant N000142112044). The authors declare that they have no conflict of interest.
Group:GALCIT
Funders:
Funding AgencyGrant Number
Office of Naval Research (ONR)N00014-21-1-2044
Subject Keywords:Digital image correlation (DIC) · Spatiotemporally adaptive · Adaptive quadtree mesh · Data-driven · Discontinuity · Complex geometry
DOI:10.1007/s11340-022-00872-4
Record Number:CaltechAUTHORS:20220725-155836000
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220725-155836000
Official Citation:Yang, J., Rubino, V., Ma, Z. et al. SpatioTemporally Adaptive Quadtree Mesh (STAQ) Digital Image Correlation for Resolving Large Deformations Around Complex Geometries and Discontinuities. Exp Mech (2022). https://doi.org/10.1007/s11340-022-00872-4
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:115816
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:27 Jul 2022 16:36
Last Modified:27 Jul 2022 16:36

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