Characterization of U-Mo Foils for AFIP-7

Twelve AFIP in-process foil samples, fabricated by either Y-12 or LANL, were shipped from LANL to PNNL for potential characterization using optical and scanning electron microscopy techniques. Of these twelve, nine different conditions were examined to one degree or another using both techniques. For this report a complete description of the results are provided for one archive foil from each source of material, and one unirradiated piece of a foil of each source that was irradiated in the Advanced Test Reactor. Additional data from two other LANL conditions are summarized in very brief form in an appendix. The characterization revealed that all four characterized conditions contained a cold worked microstructure to different degrees. The Y-12 foils exhibited a higher degree of cold working compared to the LANL foils, as evidenced by the highly elongated and obscure U-Mo grain structure present in each foil. The longitudinal orientations for both of the Y-12 foils possesses a highly laminar appearance with such a distorted grain structure that it was very difficult to even offer a range of grain sizes. The U-Mo grain structure of the LANL foils, by comparison, consisted of a more easily discernible grain structure with a mix of equiaxed and elongated grains. Both materials have an inhomogenous grain structure in that all of the characterized foils possess abnormally coarse grains

Characterization of U-Mo Foils for AFIP-7

Twelve AFIP in-process foil samples, fabricated by either Y-12 or LANL, were shipped from LANL to PNNL for potential characterization using optical and scanning electron microscopy techniques. Of these twelve, nine different conditions were examined to one degree or another using both techniques. For this report a complete description of the results are provided for one archive foil from each source of material, and one unirradiated piece of a foil of each source that was irradiated in the Advanced Test Reactor. Additional data from two other LANL conditions are summarized in very brief form in an appendix. The characterization revealed that all four characterized conditions contained a cold worked microstructure to different degrees. The Y-12 foils exhibited a higher degree of cold working compared to the LANL foils, as evidenced by the highly elongated and obscure U-Mo grain structure present in each foil. The longitudinal orientations for both of the Y-12 foils possesses a highly laminar appearance with such a distorted grain structure that it was very difficult to even offer a range of grain sizes. The U-Mo grain structure of the LANL foils, by comparison, consisted of a more easily discernible grain structure with a mix of equiaxed and elongated grains. Both materials have an inhomogenous grain structure in that all of the characterized foils possess abnormally coarse grains

Automated Grain Boundary (GB) Segmentation and Microstructural Analysis in 347H Stainless Steel Using Deep Learning and Multimodal Microscopy

Austenitic 347H stainless steel offers superior mechanical properties and corrosion resistance required for extreme operating conditions such as high temperature. The change in microstructure due to composition and process variations is expected to impact material properties. Identifying microstructural features such as grain boundaries thus becomes an important task in the process-microstructure-properties loop. Applying convolutional neural network (CNN) based deep-learning models is a powerful technique to detect features from material micrographs in an automated manner. Manual labeling of the images for the segmentation task poses a major bottleneck for generating training data and labels in a reliable and reproducible way within a reasonable timeframe. In this study, we attempt to overcome such limitations by utilizing multi-modal microscopy to generate labels directly instead of manual labeling. We combine scanning electron microscopy (SEM) images of 347H stainless steel as training data and electron backscatter diffraction (EBSD) micrographs as pixel-wise labels for grain boundary detection as a semantic segmentation task. We demonstrate that despite producing instrumentation drift during data collection between two modes of microscopy, this method performs comparably to similar segmentation tasks that used manual labeling. Additionally, we find that na\"ive pixel-wise segmentation results in small gaps and missing boundaries in the predicted grain boundary map. By incorporating topological information during model training, the connectivity of the grain boundary network and segmentation performance is improved. Finally, our approach is validated by accurate computation on downstream tasks of predicting the underlying grain morphology distributions which are the ultimate quantities of interest for microstructural characterization