# Exploring the microstructure manifold: image texture representations applied to ultrahigh carbon steel microstructures

## TL;DR

This paper applies modern image representation techniques, including neural networks and visualization methods, to analyze complex microstructures in ultrahigh carbon steel, revealing insights into their relationship with heat treatment processes.

## Contribution

It introduces a microstructure dataset and compares supervised and unsupervised image representations for classifying microstructures and processing conditions.

## Key findings

- Neural network features outperform keypoint-based methods in classification.
- t-SNE visualizations reveal microstructure relationships and processing trends.
- Machine learning can effectively interpret complex microstructural data.

## Abstract

We introduce a microstructure informatics dataset focusing on complex, hierarchical structures found in a single Ultrahigh carbon steel under a range of heat treatments. Applying image representations from contemporary computer vision research to these microstructures, we discuss how both supervised and unsupervised machine learning techniques can be used to yield insight into microstructural trends and their relationship to processing conditions. We evaluate and compare keypoint-based and convolutional neural network representations by classifying microstructures according to their primary microconstituent, and by classifying a subset of the microstructures according to the annealing conditions that generated them. Using t-SNE, a nonlinear dimensionality reduction and visualization technique, we demonstrate graphical methods of exploring microstructure and processing datasets, and for understanding and interpreting high-dimensional microstructure representations.