Microstructure classification in the unsupervised context

Traditional microstructure classification requires human annotations provided by a subject matter expert. The requirement of human input is both costly and subjective and cannot keep up with the current volume of experimentally and computationally generated microstructure images. In this work, we develop a framework that is capable of reducing the cost of human annotation in this process by leveraging novel machine learning procedures for class discovery and label assignment. To reduce the penalty of a poor label assignment made by this automated process, labels are only assigned to high-confidence observations while ambiguous data are left unlabeled. Semi-supervised classification is then employed to leverage the high-and low-confidence label assignments, and a novel generalization of an established semi-supervised error estimation technique to the multi-class context is introduced to assess the resulting classifiers. Finally, it is shown that this framework can be used to produce highly accurate classifiers over microstructure image class taxonomies which are discovered solely through data-driven methods and which display consistent structural trends within and distinct morphological differences between classes. (c) 2021 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Automated summary

The framework developed in this work reduces the cost of human annotation by leveraging novel machine learning procedures for class discovery and label assignment. It utilizes semi-supervised classification to combine high-and low-confidence label assignments, resulting in highly accurate classifiers for microstructure image class taxonomies discovered solely through data-driven methods.