|About this Abstract
||2019 TMS Annual Meeting & Exhibition
||Computational Approaches for Big Data, Artificial Intelligence and Uncertainty Quantification in Computational Materials Science
||Uncertainty Quantification in Microstructural Reconstruction of Additively Manufactured Materials
||Pinar Acar, Veera Sundararaghavan
|On-Site Speaker (Planned)
The present work addresses an analytical uncertainty quantification (UQ) methodology to model the epistemic uncertainties arising from the use of microstructure reconstruction algorithms. For reconstruction, a Markov Random Field (MRF) approach is utilized to predict the large-scale spatial distribution of a microstructure given an experimental input measured over a small spatial domain. However, small variations are observed on the microstructural features of the synthesized samples due to the randomness introduced by the MRF algorithm. The proposed analytical UQ technique aims to quantify these uncertainties and estimate their propagation to the macro-scale material properties. The solution framework is first validated by using the experimental electron backscatter diffraction (EBSD) samples measured during the compression test of a Ti-7Al alloy. The analytical solution is shown to adequately predict the stochasticity in material responses. Next, the randomness of the microstructure reconstruction is explored when the EBSD scans of an additive manufacturing process are inputted.
||Planned: Supplemental Proceedings volume