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Meeting	2021 TMS Annual Meeting & Exhibition
Symposium	AI/Data Informatics: Applications and Uncertainty Quantification at Atomistics and Mesoscales
Presentation Title	Uncertainty Quantification of Microstructures with a New Technique: Shape Moment Invariants
Author(s)	Arulmurugan Senthilnathan, Pinar Acar
On-Site Speaker (Planned)	Arulmurugan Senthilnathan
Abstract Scope	Microstructure reconstruction is an efficient strategy to predict the microstructure evolution over large domains given small-scale experimental data. However, such prediction is influenced by the effects of the uncertainties in computations and experiments. While the uncertainty quantification (UQ) of crystallographic texture and grain size is addressed with state-of-the-art methods, the UQ of grain shapes is still an unexplored research challenge. We present a novel UQ methodology for metallic microstructures that utilizes the shape moment invariants in physics to mathematically quantify the uncertainty in grain shapes. The experimental data of the aerospace alloy, Titanium-7wt%- Aluminum (Ti-7Al), is used to generate synthetic microstructures using Markov Random Field (MRF). The UQ formulation is first applied to predict the variations in the texture, grain sizes and shapes of the synthetic microstructures. Next, the propagation of the microstructural uncertainty on the elasto-plastic material properties is studied by utilizing the UQ formulation and crystal plasticity simulations.
Proceedings Inclusion?	Planned:

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