We implement an approach based on Bayesian inference to calibrate the material parameters of a constitutive model for the superelastic deformation of NiTi shape memory alloy. We use a diamond-shaped specimen geometry that is suited to calibrate both tensile and compressive material parameters from a single test. We adopt the Bayesian inference calibration scheme to take full-field surface strain measurements obtained using digital image correlation together with global load data as an input for calibration. The calibration is performed by comparing these two experimental quantities of interest with the corresponding results from a simulation library built with a superelastic forward finite element model. We enrich the simulation library using a machine-learning-based surrogate model. This improves the calibration accuracy to the extent permitted by the accuracy of the underlying material model and also improves the computational efficiency. We demonstrate, verify, and partially validate the calibration results through various examples. We also demonstrate how the uncertainty in the calibrated superelastic material parameters can propagate to a subsequent simulation of fatigue loading. This versatile approach can be used to calibrate other models of superelastic deformation from data obtained using various in-situ characterization techniques.

An Approach Based on Bayesian Inference and Machine Learning for the Calibration of a Superelastic Constitutive Model from Full-field Strain Data

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