The multifaceted nature of uncertainty in structure-property linkage with crystal plasticity finite element model

TL;DR

This paper discusses the importance of uncertainty quantification in crystal plasticity finite element models, addressing microstructural variability and model errors, and explores recent research in UQ, optimization, and machine learning for these models.

Contribution

It highlights ongoing research topics in UQ, optimization, and machine learning applications specifically for CPFEM to improve forward and inverse problem solving.

Findings

Addresses microstructure-induced uncertainty in CPFEM

Explores UQ, optimization, and machine learning methods for CPFEM

Discusses challenges in forward and inverse problem solutions

Abstract

Uncertainty quantification (UQ) plays a critical role in verifying and validating forward integrated computational materials engineering (ICME) models. Among numerous ICME models, the crystal plasticity finite element method (CPFEM) is a powerful tool that enables one to assess microstructure-sensitive behaviors and thus, bridge material structure to performance. Nevertheless, given its nature of constitutive model form and the randomness of microstructures, CPFEM is exposed to both aleatory uncertainty (microstructural variability), as well as epistemic uncertainty (parametric and model-form error). Therefore, the observations are often corrupted by the microstructure-induced uncertainty, as well as the ICME approximation and numerical errors. In this work, we highlight several ongoing research topics in UQ, optimization, and machine learning applications for CPFEM to efficiently solve forward and inverse problems.