Conference Tools for 2021 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2021 TMS Annual Meeting & Exhibition
Symposium	AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
Presentation Title	A Probabilistic Approach with Built-in Uncertainty Quantification for the Calibration of a Superelastic Constitutive Model from Full-field Strain Data
Author(s)	Harshad Paranjape, Kenneth Aycock, Craig Bonsignore, Jason Weaver, Brent Craven, Thomas Duerig
On-Site Speaker (Planned)	Harshad Paranjape
Abstract Scope	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 calibration using the full-field strain data improves the accuracy of subsequent simulations of local deformation.
Proceedings Inclusion?	Planned:

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