|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Multi-fidelity Surrogate Modeling of Epistemic Uncertainty Arising from Microstructure Reconstruction
||Arulmurugan Senthilnathan, Pinar Acar
|On-Site Speaker (Planned)
Microstructure reconstruction enables multi-scale analysis using small-scale experimental data and, thus, eliminates the infeasibilities arising from the cost and time requirements of experiments in large domains. Markov Random Field is one efficient method that produces statistically-equivalent synthetic representations to small-scale test samples while introducing epistemic uncertainty on microstructural features. These uncertainties can alter material properties by propagating over multiple scales. The present work addresses multi-scale modeling for grain topology of polycrystalline microstructures under the effects of the microstructural uncertainties. The special focus is on the Titanium-7wt\%-Aluminum alloy (Ti-7Al), which is a candidate material for many aerospace systems owing to its outstanding mechanical performance in elevated temperatures. The shape moment invariants are used to quantify the grain topology of polycrystalline microstructures and a surrogate model based on Gaussian Process Regression (GPR) is developed as a function of shape moment invariants by utilizing the experiments and crystal plasticity simulations for training data.
||Computational Materials Science & Engineering, Modeling and Simulation, Machine Learning