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Updated: Sep 9, 2025

Intermediate Strain Rate Material Characterization with Digital Image Correlation

Intermediate Strain Rate Material Characterization with Digital Image Correlation

Published on: March 1, 2019

A PHYSICS-GUIDED SMOOTHING METHOD FOR MATERIAL MODELING WITH DIGITAL IMAGE CORRELATION (DIC) MEASUREMENTS.

Jihong Wang¹, Chung-Hao Lee², William Richardson³

¹Department of Mathematics, Lehigh University, Bethlehem, PA, USA.

Proceedings. International Conference on Image Processing

|August 29, 2025

Summary

This summary is machine-generated.

We developed a new method to analyze digital image correlation (DIC) data from material stretching tests. This approach improves the accuracy of modeling complex biological tissues like heart valves.

Keywords:

DIC measurement Material modeling Physics-guided smoothing Reproducing kernel

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Last Updated: Sep 9, 2025

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Area of Science:

Biomechanics
Materials Science
Computational Mechanics

Background:

Digital Image Correlation (DIC) is crucial for measuring material deformation.
Analyzing DIC data from complex loading conditions like biaxial stretching presents challenges.
Accurate material modeling requires physically consistent strain and displacement fields.

Purpose of the Study:

To present a novel, optimization-based approach for processing DIC measurements from multiple biaxial stretching protocols.
To develop a data-driven workflow for heterogeneous material modeling using processed DIC data.
To apply and validate the approach on a biological tissue sample.

Main Methods:

An optimization-based method using moving least-squares with positive strain constraints to obtain smoothed nodal displacements.
A data-driven workflow to estimate nonlocal constitutive laws and material microstructure.
Application to DIC measurements from a porcine tricuspid valve anterior leaflet.

Main Results:

Physically consistent displacement and strain fields were obtained from DIC data.
A nonlocal constitutive law and fiber orientation field were successfully estimated.
The approach demonstrated improved accuracy in modeling biological materials.

Conclusions:

The proposed DIC data processing method enhances the accuracy of material modeling, particularly for biological tissues.
This work provides a robust framework for integrating DIC measurements with data-driven material modeling.
The methodology is effective for characterizing heterogeneous material behavior and microstructure.