High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel

We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively. We explore two microstructure segmentation tasks in an openly available ultrahigh carbon steel...

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Bibliographic Details
Published in:	Microscopy and microanalysis Vol. 25; no. 1; pp. 21 - 29
Main Authors:	DeCost, Brian L., Lei, Bo, Francis, Toby, Holm, Elizabeth A.
Format:	Journal Article
Language:	English
Published:	New York, USA Cambridge University Press 01.02.2019 Oxford University Press
Subjects:	Annotations Architectural engineering Artificial neural networks Automation Carbon steel Case depth Cemented carbides Cementite Datasets Deep learning Design Grain boundaries High carbon steels Machine learning Materials Science Applications Measurement techniques Metallography Microstructure Neural networks Photomicrographs Quantitative analysis Quantitative metallography Scientists Segmentation Semantics Spheroidizing Teaching methods deep learning steel segmentation microstructure
ISSN:	1431-9276, 1435-8115, 1435-8115
Online Access:	Get full text
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