Date of Award
3-2020
Document Type
Thesis
Degree Name
Master of Science in Nuclear Engineering
Department
Department of Engineering Physics
First Advisor
Abigail A. Bickley, PhD
Abstract
A machine learning approach is taken to characterizing a group of synthetic uranium bearing particles. SEM images of these lab-created particles were converted into a binary representation that captured morphological features in accordance with a guide established by Los Alamos National Laboratory. Each particle in the dataset contains an association with chemical creation conditions: processing method, precipitation temperature and pH, calcination temperature are most closely tied to particle morphology. Additionally, trained classifiers are able to relate final products between particles, implying that morphological features are shared between particles with similar composition.
AFIT Designator
AFIT-ENP-MS-20-M-099
DTIC Accession Number
AD1102888
Recommended Citation
Gum, Daniel A., "A Machine Learning Approach to Characterizing Particle Morphology in Nuclear Forensics" (2020). Theses and Dissertations. 3598.
https://scholar.afit.edu/etd/3598