We introduce a microstructure informatics 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.

We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively. We explore two microstructure segmentation tasks in an openly-available ultrahigh carbon steel microstructure dataset [1, 2]: segmenting cementite particles in the spheroidized matrix, and segmenting larger fields of view featuring grain boundary carbide, spheroidized particle matrix, particle-free grain boundary denuded zone, and Widmanstätten cementite. We also demonstrate how to combine these data-driven microstructure segmentation models to obtain empirical cementite particle size and denuded zone width distributions from more complex micrographs containing multiple microconstituents. The full annotated dataset is available on materialsdata.nist.gov [3].

We present a new microstructure dataset consisting of ultrahigh carbon steel (UHCS) micrographs taken over a range of length scales under systematically varied heat treatments. Using the UHCS dataset as a case study, we develop a set of visualization tools for interacting with and exploring large microstructure and metadata datasets. Based on generic microstructure representations adapted from the field of computer vision, these tools enable image-based microstructure retrieval, as well as spatial maps of both microstructure and related metadata, such as processing conditions or properties measurements. We provide the microstructure image data, processing metadata, and source code for these microstructure exploration tools. The UHCS dataset is intended as a community resource for development and evaluation of microstructure data science techniques and for creation of microstructure data science teaching modules.

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