A Probabilistic Approach with Built-in Uncertainty Quantification for the Calibration of a Superelastic Constitutive Model from Full-field Strain Data

We implement an approach using Bayesian inference and machine learning to calibrate the material parameters of a finite element constitutive model for the superelastic deformation of NiTi shape memory alloy. The calibration scheme uses full-field surface strain measurements obtained using digital image correlation and global load data from tensile tests as the inputs for calibration. We use machine learning to create a surrogate model for the finite element constitutive model. We use the surrogate model to perform the Monte Carlo sampling as part of the calibration process. 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. The machine learning surrogate model improves the computational efficiency of the calibration scheme and the use of full-field strain data improves the accuracy of subsequent simulations of local deformation.