用物理约束优化数字图像相关数据，提升生物材料建模精度
A physics-guided smoothing method for material modeling with digital image correlation (DIC) measurements
- 基于移动最小二乘法加正应变约束，生成物理一致的位移场
- 从猪三尖瓣前叶的DIC数据中学习到非局部本构关系与纤维取向
- 适合做生物软组织力学建模的研究者参考
本文提出一种新方法，用于处理多轴拉伸实验中的数字图像相关（DIC）测量数据。通过基于优化的移动最小二乘算法，在满足正应变约束条件下计算平滑节点位移，从而获得物理一致的位移与应变场。在此基础上，构建数据驱动流程，从这些物理一致的DIC数据中联合估计非局部本构关系及材料微观结构。为验证该方法的有效性，将其应用于猪三尖瓣前叶的DIC测量数据，成功学习出材料模型与纤维取向场。结果表明，所提出的DIC数据处理方法能显著提升生物材料建模的准确性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。