Advancements in Constitutive Model Calibration: Leveraging the Power of Full-Field DIC Measurements and In-Situ Load Path Selection for Reliable Parameter Inference
🤖 AI Summary
Existing material model calibration methods suffer from four key limitations: reliance on simplified specimens and global measurements, non-targeted data acquisition, deterministic optimization that neglects uncertainty quantification, and inefficient sequential workflows. This paper proposes an Interleaved Characterization and Calibration (ICC) framework—the first to implement a closed-loop workflow—integrating full-field digital image correlation (DIC), Bayesian optimal experimental design, principal component analysis (PCA)-based dimensionality reduction, finite element-based surrogate modeling, and Markov Chain Monte Carlo (MCMC)-driven uncertainty inference. The ICC framework enables demand-driven loading-path planning and real-time feedback, markedly improving information utilization efficiency. Applied to biaxial deformation calibration of an aluminum cruciform specimen, it achieves high parameter accuracy, rigorous uncertainty quantification, and over 70% reduction in computational cost. Results demonstrate that high-fidelity constitutive models can be efficiently and reliably deployed for engineering decision-making.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Improves constitutive model calibration using full-field DIC measurements
Ensures optimal experimental design for reliable parameter inference
Quantifies parameter uncertainty via Bayesian inference methods
Innovation
Methods, ideas, or system contributions that make the work stand out.
Uses full-field DIC measurements for calibration
Applies Bayesian optimal experimental design
Employs PCA and surrogate models for efficiency
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