Overview: Computer vision and machine learning for microstructural characterization and analysis
It addresses the problem of labor-intensive and limited traditional methods in materials science by providing an overview of automated tools, though it is incremental as it summarizes existing advances.
This paper surveys how computer vision and machine learning can automate and enhance microstructural analysis by extracting visual information from images and using ML algorithms to find patterns, enabling new metrics and relationships.
The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This overview surveys CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis span the taxonomy of image analysis tasks, including image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new, rich visual metrics and the discovery of processing-microstructure-property relationships.