@article{pazdernik_lahaye_artman_zhu_2020, title={Microstructural classification of unirradiated LiAlO2 pellets by deep learning methods}, volume={181}, ISSN={["1879-0801"]}, DOI={10.1016/j.commatsci.2020.109728}, abstractNote={Microstructural features and defects can greatly impact material properties and performance in a wide range of application areas. Recognition and characterization of microstructural features is essential to the understanding and prediction of material performance under various operational conditions, including irradiation. In this work, we tested a collection of Deep Convolutional Neural Network (DCNN) architectures that have been optimized for image segmentation and selected the best performer to obtain pixel-level classification of the main microstructural features in unirradiated LiAlO2 pellets, including grains, grain boundaries, voids, precipitates, and zirconia impurities. LiAlO2 is an important material that is used as a tritium producer for the Tritium Sustainment Program. While LiAlO2 pellets have been employed in tritium-producing burnable absorber rods (TPBARs) for years, comprehensive microstructural analysis of unirradiated LiAlO2, and therefore time-dependent tritium release from the material during irradiation, has not been established. A full understanding of unirradiated LiAlO2 microstructure and how it evolves as a result of neutron irradiation is necessary to produce an integrated performance model to predict in-reactor behavior as well as to target strategic experiments. This work aims at developing a fast and quantitative analysis method to classify various microstructural features in unirradiated LiAlO2 pellets that are visualized by scanning electron microscopy (SEM). Given classification results obtained, statistical analysis was then carried out to evaluate the performance of the DCNN classification and to describe the properties of the microstructural features as a whole, based on standard aggregation and spatial point-process methodology. Our results show improved performance over a baseline heuristic approach. Also, the computational efficiency of the computer-aided analytical method allows for quantitative characterization of a larger volume of SEM images than was previously possible using manual segmentation.}, journal={COMPUTATIONAL MATERIALS SCIENCE}, author={Pazdernik, Karl and LaHaye, Nicole L. and Artman, Conor M. and Zhu, Yuanyuan}, year={2020}, month={Aug} }