We introduce a microstructure dataset focusing on complex, hierarchical structures found in a single Ultrahigh carbon steel under a range of heat treatments. Applying image representations from contemporary computer vision research to these microstructures, we discuss how both supervised and unsupervised machine learning techniques can be used to yield insight into microstructural trends and their relationship to processing conditions. We evaluate and compare keypoint-based and convolutional neural network representations by classifying microstructures according to their primary microconstituent, and by classifying a subset of the microstructures according to the annealing conditions that generated them. Using t-SNE, a nonlinear dimensionality reduction and visualization technique, we demonstrate graphical methods of exploring microstructure and processing datasets, and for understanding and interpreting high-dimensional microstructure representations.
DeCost, B. L., Francis, T., & Holm, E. A. (2017). Exploring the microstructure manifold: Image texture representations applied to ultrahigh carbon steel microstructures. Acta Materialia, 133, 30–40. https://doi.org/10.1016/j.actamat.2017.05.014