A Machine Learning Approach to Characterizing Particle Morphology in Nuclear Forensics

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.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2020
Accession Number
AD1102888

Entities

People

  • Daniel A. Gum

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Air Force
  • Artificial Intelligence
  • Artificial Intelligence Software
  • Artificial Neural Networks
  • Bayesian Networks
  • Chemical Engineering
  • Chemistry
  • Data Mining
  • Data Science
  • Dimensionality Reduction
  • Information Science
  • Machine Learning
  • Materials
  • Materials Processing
  • Materials Science
  • Materials Testing
  • Neural Networks
  • Nuclear Energy
  • Nuclear Materials
  • Particles
  • Probabilistic Models
  • Supervised Machine Learning
  • Uranium Compounds

Readers

  • Aerosol Science/Aerosol Physics
  • Neural Network Machine Learning.
  • Powder metallurgy of Titanium alloys.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks