Session: 03-12-02: Testing and Characterization

Paper Number: 136911

136911 - Unveiling Full-Field Modulus and Stress Using Digital Image Correlation 

Digital Image Correlation (DIC) is a widely used technique in experimental labs for assessing full-field displacement. However, its potential extends beyond this setting. This presentation advances the possibilities of characterizing a spatially varying modulus field by integrating DIC with a partial differential equation (PDE)-based inverse problem, forming a unified joint inversion. With images and boundary conditions it will be shown that displacement and modulus fields can be discovered, naturally extending to stress fields. The PDE describes the expected forward physics; this presentation will show examples with linear elasticity and hyperelasticity. Our exploration reveals that handling the joint inverse problem within an infinite-dimensional framework (using adjoint-based gradients and Hessians) allows for the emergence of heterogeneity or defects in the solution. We will present results from various regularization methods, incorporated to mitigate the ill-posed nature of the inverse problem. Notably, total variation (TV) will be highlighted for its effectiveness in preserving sharp interfaces found in various materials like fibers, particles, and grain boundaries. The presentation will elaborate on a primal-dual TV formulation designed to maintain mesh-independence. Finally, we will discuss inversion results obtained from experimental coupons 3D printed via Polyjet, where the as-printed color corresponds to modulus. To the best of our knowledge, the algorithm presented here represents a pioneering effort in inferring modulus as a fully heterogeneous parameter.

Presenting Author: Joseph Kirchhoff The University of Texas at Austin (Walker, Oden)

Presenting Author Biography: I am a Ph.D. student in the Walker School of Mechanical Engineering where I am co-advised by Dr. Omar Ghattas and Dr. Mehran Tehrani. Funded by a NASA NSTGRO fellowship, I am interested in developing industry 4.0 tools for composites manufacturing. Specifically, the technology I aim to enhance with computational tools is In-Situ Consolidation of Thermoplastic Composites (ICAT). ICAT has potential to be both a high-rate and sustainable approach. I balance my time running experiments in a lab and programming computational methods. Some key areas of interest are: real-time bayesian inversion, uncertainty quantification, fiber composite, solid mechanics, and materials characterization.