Conference Tools for 2020 TMS Annual Meeting & Exhibition

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About this Abstract
Meeting	2020 TMS Annual Meeting & Exhibition
Symposium	Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
Presentation Title	Machine Learning Reinforced Crystal Plasticity Modeling of Titanium-Aluminum Alloys under Uncertainty
Author(s)	Pinar Acar
On-Site Speaker (Planned)	Pinar Acar
Abstract Scope	We present a two-step computational approach to achieve a robust crystal plasticity model for Titanium-Aluminum alloys by considering experimental uncertainty and using machine learning techniques. The two-step solution involves the validation of the global (component-scale) and local (grain-level) features with the experimental data. The first step identifies the lower and upper bounds of the unknown crystal plasticity parameters through an inverse problem that aims to match the computations with the stress-strain response test data. The second step validates the local features with surrogate-based optimization by minimizing the difference between the simulated and experimental microstructural textures. The Artificial Neural Network is used to generate the computationally efficient surrogate representation of the crystal plasticity model to simulate the microstructural texture. With this machine-learning reinforced two-step solution approach under uncertainty, we achieve significant improvements on the accurate representation of global and local microstructural features, over the previous deterministic solutions in the literature.
Proceedings Inclusion?	Planned: Supplemental Proceedings volume

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