|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||A Data-driven Approach for Improving the Existing Gurson Material Damage Model Using Genetic Programming for Symbolic Regression
||Donovan Birky, Jacob Zamora, John Emery, Coleman Alleman, Brian Lester, Geoffrey Bomarito, Jacob Hochhalter
|On-Site Speaker (Planned)
Traditional material constitutive models have the advantages of being interpretable, flexible, and computationally tractable, but can be limited in their accuracy by the assumptions required to formulate them. Future data-driven constitutive models should maintain these characteristics while improving accuracy by taking advantage of data that can be readily generated. In an attempt to address this need, the use of genetic programming for symbolic regression (GPSR) is demonstrated. GPSR uses machine learning to create inherently interpretable models trained on experimental or simulation data. In this study, finite element (FE) simulations of representative microstructures are used to extend the Gurson damage model for porous ductile materials. Assumptions made in the original Gurson model are systematically relaxed to understand changes in the models to enable interpretability. The results show an improved accuracy from Gurson’s prediction, and the resulting model form allows conjecture of decreased material strength due to void interaction and non-symmetric void-shapes.
||Machine Learning, Other, Other