Journal
ACTA MATERIALIA
Volume 133, Issue -, Pages 30-40Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.actamat.2017.05.014
Keywords
Multiscale; Microstructure; Processing; Steels; Computer vision
Funding
- National Science Foundation [DMR-1307138, DMR-1501830, CMMI-1436064]
- John and Claire Bertucci Foundation
- Commonwealth of Pennsylvania Department of Community and Economic Development (DCED) Developed in PA program (D2PA)
- Direct For Mathematical & Physical Scien
- Division Of Materials Research [1307138] Funding Source: National Science Foundation
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We introduce a microstructure 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 micro constituent, 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. (C) 2017 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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