AUTHOR=Manda Haarika , Zhao Liang , Reddy Kancharla Rahul , Xiao Xianghui , Purushotham Charith , Tang Yalei , Xu Peng , Yao Tiankai , Xu Fei 

TITLE=Bridging multimodal microscopy for advanced characterization on nuclear fuel using machine learning

JOURNAL=Frontiers in Mechanical Engineering

VOLUME=Volume 11 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/mechanical-engineering/articles/10.3389/fmech.2025.1619834

DOI=10.3389/fmech.2025.1619834

ISSN=2297-3079

ABSTRACT=Uranium dioxide (UO2), widely used as driver fuel in light water reactors, experiences microstructure and property change by nuclear fission reactions. This paper bridges the characterization of fresh UO2 fuel at different length scales, serving as a baseline for future post irradiation examination of irradiated UO2 fuel. To characterize the microstructural change of nuclear fuel, modern approaches cover a wide range of length scales through different characterization techniques, such as mm scale for Synchrotron-based X-ray computed tomography (SXCT) and microscale for focused ion beam (FIB) and scanning electron microscopy (SEM). It is challenging to bridge the data and knowledge of the same sample in different length scales. This paper proposed a deep learning framework leveraging transfer learning to detect microstructural defects, trained from a sparse FIB, SEM, and SXCT images. The proposed model achieved superior performance in defect segmentation on multiscale microscopic data compared to four of the latest deep learning models.