Overview: Computer vision and machine learning for microstructural characterization and analysis

TL;DR

This paper reviews how computer vision and machine learning techniques are transforming microstructural characterization by enabling automated, high-dimensional analysis and discovery of relationships in materials science.

Contribution

It provides a comprehensive overview of CV and ML methods applied to microstructural image analysis, highlighting new tools and approaches for materials characterization.

Findings

CV/ML enable automated microstructural analysis

New visual metrics for microstructure characterization

Discovery of processing-microstructure-property relationships

Abstract

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.