Advancements in Constitutive Model Calibration: Leveraging the Power of Full-Field DIC Measurements and In-Situ Load Path Selection for Reliable Parameter Inference

TL;DR

This paper introduces an advanced workflow called Interlaced Characterization and Calibration (ICC) that uses full-field data, optimal experimental design, Bayesian uncertainty quantification, and real-time feedback to improve material model calibration.

Contribution

The paper presents the ICC framework, integrating full-field data, optimal load path selection, Bayesian inference, and surrogate modeling for efficient, reliable, and uncertainty-aware constitutive model calibration.

Findings

Efficient calibration of high-fidelity models using full-field data.

Optimal experimental design improves information gain.

Uncertainty quantification enhances model reliability.

Abstract

Accurate material characterization and model calibration are essential for computationally-supported engineering decisions. Current characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data is collected for a specific model of interest, (3) use deterministic methods that provide best-fit parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work brings together several recent advancements into an improved workflow called Interlaced Characterization and Calibration that advances the state-of-the-art in constitutive model calibration. The ICC paradigm (1) efficiently uses full-field data to calibrate a high-fidelity material model, (2) aligns the data needed with the data collected with an optimal experimental design protocol, (3) quantifies parameter uncertainty through Bayesian inference, and (4) incorporates these advances into a quasi real-time feedback loop. The ICC framework is demonstrated on the calibration of a material model using simulated full-field data for an aluminum cruciform specimen being deformed bi-axially. The cruciform is actively driven through the myopically optimal load path using Bayesian optimal experimental design, which selects load steps that yield the maximum expected information gain. To aid in numerical stability and preserve computational resources, the full-field data is dimensionally reduced via principal component analysis, and fast surrogate models which approximate the input-output relationships of the expensive finite element model are used. The tools demonstrated here show that high-fidelity constitutive models can be efficiently and reliably calibrated with quantified uncertainty, thus supporting credible decision-making and potentially increasing the agility of solid mechanics modeling.