September 20, 2023
Conference Paper

TopTemp: Parsing Precipitate Structure from Temper Topology


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.

Published: September 20, 2023


Kassab L., S.A. Howland, H.J. Kvinge, K.S. Kappagantula, and T.H. Emerson. 2022. TopTemp: Parsing Precipitate Structure from Temper Topology. In ICML Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG:ML 2022), July 20. 2022, Virtual, Online. Proceedings of Machine Learning Research, edited by A. Cloninger, et al, 196, 199 - 205. Maastricht:ML Research Press. PNNL-SA-170620.