Digital fingerprinting of microstructures

Finding efficient means of quantitatively describing material microstructure is a critical step towards harnessing data-centric machine learning approaches to understanding and predicting processing–microstructure–property relationships. Current quantitative descriptors of microstructure tend to consider only specific, narrow features such as grain size or phase fractions, but these metrics discard vast amounts of information. Since the gain in traction of machine learning and computer vision, more abstract methods for describing image data in a concise and quantitative manner have become available, but have yet to be fully exploited within materials science. The main aim of this paper is to investigate some of these methods as tools for constructing compressed numerical descriptions of microstructural image data, which are here referred to as "microstructural fingerprints". A statistical framework is developed to combine some of these methods which includes some classical computer vision methods as special cases. The effectiveness of fingerprints, in this case, is assessed via a series of classification tasks, which take the fingerprints as input along with some label to describe the microstructure, and aim to predict the class label. The classification tasks are a simple way to assess the information content of differently constructed fingerprints, using readily available data. However, the potential applications for such fingerprints, in theory, extend far beyond this, provided a suitably labelled training dataset. For example, fingerprints constructed from image data labelled with some processing parameters or mechanical properties could be fed into regression-based tasks to predict properties from microstructure or predict the processing parameters required for a target microstructure. The ultimate purpose is to rapidly fingerprint sample images in the context of various high-throughput design/make/test scenarios. Such fingerprints would enable, for example, quantification of the disparity between microstructures for quality control, classifying microstructures, predicting materials properties from image data and identifying potential processing routes to engineer new materials with specific properties. Here, the approach is applied to two distinct datasets to illustrate various aspects of the fingerprints and some recommendations are made based on the findings. In particular, methods that leverage transfer learning with convolutional neural networks (CNNs), pretrained on the ImageNet dataset, are generally shown to outperform other methods. Additionally, dimensionality reduction of these CNN-based fingerprints is shown to have negligible impact on classification accuracy for the supervised learning approaches considered. In situations where there is a large dataset with only a handful of images labelled, graph-based label propagation to unlabelled data is shown to be favourable over discarding unlabelled data and performing supervised learning. In particular, label propagation by Poisson learning is shown to be highly effective at low label rates. The supplementary code is available on GitHub (White and Tarakanov, 2021).