  Micrograph quantification is an essential component of several materials
science studies. Machine learning methods, in particular convolutional neural
networks, have previously demonstrated performance in image recognition tasks
across several disciplines (e.g. materials science, medical imaging, facial
recognition). Here, we apply these well-established methods to develop an
approach to microstructure quantification for kinetic modeling of a
discontinuous precipitation reaction in a case study on the uranium-molybdenum
system. Prediction of material processing history based on image data
(classification), calculation of area fraction of phases present in the
micrographs (segmentation), and kinetic modeling from segmentation results were
performed. Results indicate that convolutional neural networks represent
microstructure image data well, and segmentation using the k-means clustering
algorithm yields results that agree well with manually annotated images.
Classification accuracies of original and segmented images are both 94\% for a
5-class classification problem. Kinetic modeling results agree well with
previously reported data using manual thresholding. The image quantification
and kinetic modeling approach developed and presented here aims to reduce
researcher bias introduced into the characterization process, and allows for
leveraging information in limited image data sets.
