TopTemp: Parsing Precipitate Structure from Temper Topology

Lara Kassab, Scott Howland, Henry Kvinge, Keerti Sahithi Kappagantula, Tegan Emerson
Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022, PMLR 196:199-205, 2022.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v196-kassab22a, title = {TopTemp: Parsing Precipitate Structure from Temper Topology}, author = {Kassab, Lara and Howland, Scott and Kvinge, Henry and Kappagantula, Keerti Sahithi and Emerson, Tegan}, booktitle = {Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022}, pages = {199--205}, year = {2022}, editor = {Cloninger, Alexander and Doster, Timothy and Emerson, Tegan and Kaul, Manohar and Ktena, Ira and Kvinge, Henry and Miolane, Nina and Rieck, Bastian and Tymochko, Sarah and Wolf, Guy}, volume = {196}, series = {Proceedings of Machine Learning Research}, month = {25 Feb--22 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v196/kassab22a/kassab22a.pdf}, url = {https://proceedings.mlr.press/v196/kassab22a.html}, 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.} }
Endnote
%0 Conference Paper %T TopTemp: Parsing Precipitate Structure from Temper Topology %A Lara Kassab %A Scott Howland %A Henry Kvinge %A Keerti Sahithi Kappagantula %A Tegan Emerson %B Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022 %C Proceedings of Machine Learning Research %D 2022 %E Alexander Cloninger %E Timothy Doster %E Tegan Emerson %E Manohar Kaul %E Ira Ktena %E Henry Kvinge %E Nina Miolane %E Bastian Rieck %E Sarah Tymochko %E Guy Wolf %F pmlr-v196-kassab22a %I PMLR %P 199--205 %U https://proceedings.mlr.press/v196/kassab22a.html %V 196 %X 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.
APA
Kassab, L., Howland, S., Kvinge, H., Kappagantula, K.S. & Emerson, T.. (2022). TopTemp: Parsing Precipitate Structure from Temper Topology. Proceedings of Topological, Algebraic, and Geometric Learning Workshops 2022, in Proceedings of Machine Learning Research 196:199-205 Available from https://proceedings.mlr.press/v196/kassab22a.html.

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