TopTemp: Parsing Precipitate Structure from Temper Topology

TL;DR

This paper introduces TopTemp, a topological method for classifying and analyzing microstructures from heat-treatment images, improving understanding of manufacturing processes with limited data and robustness to perturbations.

Contribution

TopTemp provides a novel topological representation for microstructure analysis, outperforming deep learning baselines and enhancing interpretability in process-property relationships.

Findings

Supports accurate temper classification with limited data

Generalizes well to unseen samples

Robust to image perturbations

Abstract

Technological advances are in part enabled by the development of novel manufacturing processes that give rise to new materials or material property improvements. Development and evaluation of new manufacturing methodologies is labor-, time-, and resource-intensive expensive due to complex, poorly defined relationships between advanced manufacturing process parameters and the resulting microstructures. In this work, we present a topological representation of temper (heat-treatment) dependent material micro-structure, as captured by scanning electron microscopy, called TopTemp. We show that this topological representation is able to support temper classification of microstructures in a data limited setting, generalizes well to previously unseen samples, is robust to image perturbations, and captures domain interpretable features. The presented work outperforms conventional deep learning baselines and is a first step towards improving understanding of process parameters and resulting material properties.