Accurate Segmentation of Localized Fuel Cladding Chemical Interaction Layers in SEM Micrographs with Deep Learning Method

U-Zr metallic fuels are promising fuel candidates for fast reactor applications. Fuel/cladding chemical interaction (FCCI) is a random, localized, complex interdiffusion phenomenon occurring at the fuel cladding interface under irradiation, thinning the cladding wall. This interaction has been recognized as a limiting factor in deploying metal fuels to achieve higher burnup under steady state operations. Post irradiation examination (PIE) of EBR-II and FFTF fuel pins with different irradiation conditions have been the primary method for investigating FCCI in metal fuels, utilizing various characterization techniques, including scanning electron microscopy (SEM). This study compared several computer vision and machine learning approaches for the automated segmentation of FCCI layers in SEM micrographs. We deployed and compared state-of-the-art machine learning models for the task of FCCI layer segmentation in SEM micrographs. A deep learning based end-to-end method proved its capability to enable rapid and accurate segmentation of FCCI in as-collected SEM micrographs, making it highly suitable for real-time applications to automate data analysis during data collection. Furthermore, the method reported in this study is extendable to segmentation tasks for other materials with similar resolution, texture, and contrast characteristics, paving the way for accelerated and automated analysis in characterization analysis and beyond.