A physics-guided smoothing method for material modeling with digital image correlation (DIC) measurements
This work addresses the challenge of accurately modeling heterogeneous biological materials from DIC data, which is incremental as it builds on existing DIC and material modeling techniques.
The paper tackles the problem of processing digital image correlation (DIC) measurements for material modeling by developing a physics-guided smoothing method that ensures physically consistent displacement and strain fields, and applies it to a porcine tricuspid valve leaflet, showing significant accuracy improvements in modeling biological materials.
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.