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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Vydáno v:Microscopy and microanalysis Ročník 25; číslo 1; s. 21 - 29
Hlavní autoři: DeCost, Brian L., Lei, Bo, Francis, Toby, Holm, Elizabeth A.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, USA Cambridge University Press 01.02.2019
Oxford University Press
Témata:
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.
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ISSN:1431-9276
1435-8115
1435-8115
DOI:10.1017/S1431927618015635