Microstructure-sensitive uncertainty quantification for crystal plasticity finite element constitutive models using stochastic collocation methods
Anh Tran, Tim Wildey, Hojun Lim

TL;DR
This paper applies stochastic collocation methods to quantify uncertainty in crystal plasticity finite element models, analyzing the robustness of different constitutive models across various crystal structures and sensitivities.
Contribution
It introduces a stochastic collocation approach to rigorously quantify uncertainty and sensitivity in CPFEM constitutive models for different crystal structures.
Findings
Quantified uncertainty in stress-strain predictions for various models.
Identified key parameters influencing initial yield behavior.
Provided insights for robust model calibration.
Abstract
Uncertainty quantification (UQ) plays a major role in verification and validation of computational engineering models and simulations, and establishes trust in the predictive capability of computational models. In the materials science and engineering context, where the process-structure-property-performance linkage is well known to be the only road mapping from manufacturing to engineering performance, numerous integrated computational materials engineering (ICME) models have been developed across a wide spectrum of length-scales and time-scales to relieve the burden of resource-intensive experiments. Within the structure-property linkage, crystal plasticity finite element method (CPFEM) models have been widely used since they are one of a few ICME toolboxes that allow numerical predictions, providing the bridge from microstructure to properties and performances. Several constitutive…
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Taxonomy
TopicsProbabilistic and Robust Engineering Design · Microstructure and mechanical properties · Integrated Circuits and Semiconductor Failure Analysis
