About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
Development of a New Shape Descriptor for Modeling and Uncertainty Quantification of Microstructures |
Author(s) |
Arulmurugan Senthilnathan, Pinar Acar |
On-Site Speaker (Planned) |
Arulmurugan Senthilnathan |
Abstract Scope |
The grain topology of polycrystalline microstructures has a critical influence on mechanical properties, but its mathematical quantification remains a challenge due to the complexity of grain shapes. This work develops a universal descriptor that quantifies 2D and 3D grain shapes of microstructures. In particular, it is derived by the eigenvalues that are the functions of Hu moments, which are invariant to shape transformations. The descriptor is also integrated into a novel uncertainty quantification (UQ) methodology to capture the uncertainty in grain shapes of additively manufactured microstructures and model their propagation on mechanical properties. To generate sufficient statistics, Markov Random Field (MRF) is applied to build the synthetic microstructure data for the aerospace alloy, Titanium-7wt%- Aluminum (Ti-7Al) using small-scale experimental data. The UQ formulation is used to predict the variations in the texture and grain shapes of the microstructures, as well as the elasto-plastic material properties computed by crystal plasticity simulations. |
Proceedings Inclusion? |
Planned: |
Keywords |
Characterization, Machine Learning, ICME |