  We investigate methods of microstructure representation for the purpose of
predicting processing condition from microstructure image data. A binary alloy
(uranium-molybdenum) that is currently under development as a nuclear fuel was
studied for the purpose of developing an improved machine learning approach to
image recognition, characterization, and building predictive capabilities
linking microstructure to processing conditions. Here, we test different
microstructure representations and evaluate model performance based on the F1
score. A F1 score of 95.1% was achieved for distinguishing between micrographs
corresponding to ten different thermo-mechanical material processing
conditions. We find that our newly developed microstructure representation
describes image data well, and the traditional approach of utilizing area
fractions of different phases is insufficient for distinguishing between
multiple classes using a relatively small, imbalanced original data set of 272
images. To explore the applicability of generative methods for supplementing
such limited data sets, generative adversarial networks were trained to
generate artificial microstructure images. Two different generative networks
were trained and tested to assess performance. Challenges and best practices
associated with applying machine learning to limited microstructure image data
sets is also discussed. Our work has implications for quantitative
microstructure analysis, and development of microstructure-processing
relationships in limited data sets typical of metallurgical process design
studies.
