|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Uncertainty Quantified Parametrically Homogenized Constitutive Models for Multi-scale Predictions of Fatigue Crack Nucleation in Ti Alloys
||Somnath Ghosh, Shravan Kotha, Deniz Ozturk
|On-Site Speaker (Planned)
This paper develops an uncertainty quantified, parametrically homogenized constitutive model (UQ-PHCM) for microstructure-sensitive simulation of deformation and fatigue crack nucleation in components of Ti alloys, viz. Ti-7Al and Ti64. The PHCMs are thermodynamically consistent, macroscopic constitutive models, whose parameters are explicit functions of Representative Aggregated Microstructural Parameters or (RAMPs) of microstructural morphology and crystallography. Machine learning tools operate on datasets generated by CPFEM to obtain these functional forms. A significantly reduced number of solution variables in the PHCM simulations make them several orders of magnitude more efficient with good accuracy. The UQ-PHCM framework is based on Bayesian inference to derive probabilistic microstructure-dependent constitutive laws of the macroscopic material response. The framework addresses three sources of uncertainty that accrue at the model development and response prediction stages, viz: (i) model reduction error, (ii) data sparsity, and (iii) microstructural variability. The validated UQ-PHCM is implemented to test its viability in real applications.