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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Vydané v:	Microscopy and microanalysis Ročník 25; číslo 1; s. 21 - 29
Hlavní autori:	DeCost, Brian L., Lei, Bo, Francis, Toby, Holm, Elizabeth A.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York, USA Cambridge University Press 01.02.2019 Oxford University Press
Predmet:	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
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Popis
Shrnutí:	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 microstructure dataset: segmenting cementite particles in the spheroidized matrix, and segmenting larger fields of view featuring grain boundary carbide, spheroidized particle matrix, particle-free grain boundary denuded zone, and Widmanstätten cementite. We also demonstrate how to combine these data-driven microstructure segmentation models to obtain empirical cementite particle size and denuded zone width distributions from more complex micrographs containing multiple microconstituents. The full annotated dataset is available on materialsdata.nist.gov.
Bibliografia:	ObjectType-Case Study-2 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Feature-4 ObjectType-Report-1 ObjectType-Article-3 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	1431-9276 1435-8115 1435-8115
DOI:	10.1017/S1431927618015635