Abstract Scope |
LiAlO2 is an important material that is used as a tritium producer for the Tritium Sustainment Program. To better understand the tritium release from the material during irradiation, comprehensive microstructural analysis of unirradiated and irradiated LiAlO2 is required. Recently, deep learning has been employed as a fast approach to classifying various microstructural features in LiAlO2 pellets that are visualized by scanning electron microscopy (SEM), including grains, grain boundaries, voids, precipitates, and zirconia impurities. While these methods produce high overall accuracy, the boundaries between microstructural features are predicted with higher error. Since aggregate characteristics, such as defect area and relative proportion, are highly dependent on these boundaries, these errors should not be ignored. To address this concern, we present a Bayesian deep learning approach to uncertainty quantification in the semantic segmentation of SEM images. We highlight areas where prediction is less certain and discuss the impact on physics-based modeling. |