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Meeting	2024 TMS Annual Meeting & Exhibition
Symposium	Materials Informatics to Accelerate Nuclear Materials Investigation
Presentation Title	Characterizing Microstructures in Aluminide Coatings Captured in SEM Image with Convolutional Neural Networks
Author(s)	Cuong Ly, Joshua A Silverstein, Danny J Edwards, Marjolein T Oostrom, Karl T Pazdernik
On-Site Speaker (Planned)	Cuong Ly
Abstract Scope	A hydrogen isotope, tritium, is produced by the neutron irradiation process of 6Li enriched LiAlO2 pellets in Tritium Producing Burnable Absorber Rods (TPBARs). TPBARs consist of multiple concentric tubes in which the outer tube, cladding, is coated with aluminide to prevent the release of tritium. Understanding microstructural characteristics of various layers within the aluminide coatings provides crucial insights into the quality and toughness of the cladding under radiation exposures. Hence, in this work, we propose an automatic pipeline to quantitatively characterize microstructures in the aluminide coatings captured in scanning electron microscopy (SEM) images. Concretely, we utilize an unsupervised deep convolutional neural network (CNN) to segment out various layers within the aluminide coatings and estimate their thicknesses. Next, we deploy a supervised CNN to segment various microstructures within each layer and describe the microstructural properties using a collection of statistical summaries.
Proceedings Inclusion?	Planned:
Keywords	Machine Learning, Characterization, Nuclear Materials

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