A physics-guided smoothing method for material modeling with digital image correlation (DIC) measurements
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916756780482560 |
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| author | Wang, Jihong Lee, Chung-Hao Richardson, William Yu, Yue |
| author_facet | Wang, Jihong Lee, Chung-Hao Richardson, William Yu, Yue |
| contents | In this work, we present a novel approach to process the DIC measurements of multiple biaxial stretching protocols. In particular, we develop a optimization-based approach, which calculates the smoothed nodal displacements using a moving least-squares algorithm subject to positive strain constraints. As such, physically consistent displacement and strain fields are obtained. Then, we further deploy a data-driven workflow to heterogeneous material modeling from these physically consistent DIC measurements, by estimating a nonlocal constitutive law together with the material microstructure. To demonstrate the applicability of our approach, we apply it in learning a material model and fiber orientation field from DIC measurements of a porcine tricuspid valve anterior leaflet. Our results demonstrate that the proposed DIC data processing approach can significantly improve the accuracy of modeling biological materials. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_18784 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A physics-guided smoothing method for material modeling with digital image correlation (DIC) measurements Wang, Jihong Lee, Chung-Hao Richardson, William Yu, Yue Image and Video Processing Materials Science Machine Learning In this work, we present a novel approach to process the DIC measurements of multiple biaxial stretching protocols. In particular, we develop a optimization-based approach, which calculates the smoothed nodal displacements using a moving least-squares algorithm subject to positive strain constraints. As such, physically consistent displacement and strain fields are obtained. Then, we further deploy a data-driven workflow to heterogeneous material modeling from these physically consistent DIC measurements, by estimating a nonlocal constitutive law together with the material microstructure. To demonstrate the applicability of our approach, we apply it in learning a material model and fiber orientation field from DIC measurements of a porcine tricuspid valve anterior leaflet. Our results demonstrate that the proposed DIC data processing approach can significantly improve the accuracy of modeling biological materials. |
| title | A physics-guided smoothing method for material modeling with digital image correlation (DIC) measurements |
| topic | Image and Video Processing Materials Science Machine Learning |
| url | https://arxiv.org/abs/2505.18784 |