|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Uncertainty Quantification Framework for Robust Design of Fatigue Critical Alloys
||Gary Whelan, David L. McDowell, Sam Sorkin, Jiadong Gong
|On-Site Speaker (Planned)
ICME facilitates efficient design and development of new materials, as well as optimization of existing materials. However, uncertainty is prevalent in computational modeling workflows, particularly for extreme phenomena such as fatigue. Therefore, uncertainty quantification is a critical step to achieve effective use of modeling results to support robust materials design. This work demonstrates a computational framework to address uncertainty quantification and propagation across process-structure-property (PSP) linkages for robust optimization of engineering alloys in fatigue critical applications. Process-structure linkages are modeled using CALPHAD and structure-property linkages are modeled using the crystal plasticity finite element method (CPFEM). Both epistemic and aleatory uncertainties are quantified and propagated across the entire PSP domain. Reduced-order surrogate models are trained using high-fidelity CPFEM models to facilitate rapid propagation of uncertainty and inductive design exploration across structure-property linkages. This framework is demonstrated by carrying out a robust optimization of the Ti64 material system for biaxial fatigue conditions.
||ICME, Machine Learning, Other