Microstructural characterization and analysis using computer vision and machine learning

Microstructural images encode rich data sets that contain information about the structure, processing, and properties of the parent material. As such, they are amenable to characterization and analysis by data science approaches, including computer vision (CV) and machine learning (ML). In fact, they offer certain advantages compared to natural images, often requiring smaller training data sets and enabling more thorough assessment of results. Because CV and ML methods can be trained to reproduce human visual judgments, they can perform qualitative and quantitative characterization of complex microstructures, including segmentation, measurement, classification, and visual similarity tasks, in an objective, repeatable, and indefatigable manner. In addition, we can apply these approaches to develop new characterization techniques that capitalize on the unique capabilities of computers to capture additional information compared to traditional metrics. For example, unsupervised ML can be used to identify clusters of similar microstructural features; the histogram of feature types quantitatively represents the microstructure in terms of feature size, morphology, and visual character. Finally, ML can learn to associate microstructural features with materials processing or property metadata, providing physical insight into phenomena such as strength and failure.