ORIGINAL RESEARCH article
Front. Mech. Eng.
Sec. Digital Manufacturing
Volume 11 - 2025 | doi: 10.3389/fmech.2025.1619834
This article is part of the Research TopicAdvances In AI And Machine Learning For Nuclear ApplicationsView all articles
Bridging Multimodal Microscopy for Advanced Characterization on Nuclear Fuel Using Machine Learning
Provisionally accepted
- 1Idaho National Laboratory, Idaho Falls, United States
- 2University of California Santa Barbara, Santa Barbara, United States
- 3Idaho National Laboratory (DOE), Idaho Falls, United States
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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 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.
Keywords: Nuclear Fuels, Uranium dioxide (UO2), deep learning, Transfer Learning, multimodal microscopy
Received: 28 Apr 2025; Accepted: 08 Sep 2025.
Copyright: © 2025 Manda, Zhao, Tang, Reddy Kancharla, Xu, Yao and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Fei Xu, Idaho National Laboratory (DOE), Idaho Falls, United States
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