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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| Published in: | Microscopy and microanalysis Vol. 25; no. 1; pp. 21 - 29 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
New York, USA
Cambridge University Press
01.02.2019
Oxford University Press |
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| ISSN: | 1431-9276, 1435-8115, 1435-8115 |
| Online Access: | Get full text |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| Author | Lei, Bo Francis, Toby Holm, Elizabeth A. DeCost, Brian L. |
| Author_xml | – sequence: 1 givenname: Brian L. orcidid: 0000-0002-3459-5888 surname: DeCost fullname: DeCost, Brian L. email: brian.decost@nist.gov organization: 1Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg MD, 20899, USA – sequence: 2 givenname: Bo orcidid: 0000-0001-6011-5262 surname: Lei fullname: Lei, Bo organization: 2Materials Science and Engineering, Carnegie Mellon University, Pittsburgh PA, 15213, USA – sequence: 3 givenname: Toby orcidid: 0000-0001-5665-7683 surname: Francis fullname: Francis, Toby organization: 2Materials Science and Engineering, Carnegie Mellon University, Pittsburgh PA, 15213, USA – sequence: 4 givenname: Elizabeth A. orcidid: 0000-0003-3064-5769 surname: Holm fullname: Holm, Elizabeth A. organization: 2Materials Science and Engineering, Carnegie Mellon University, Pittsburgh PA, 15213, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30869574$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright © Microscopy Society of America 2019 |
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| Keywords | deep learning steel segmentation microstructure |
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| SubjectTerms | 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 |
| Title | High Throughput Quantitative Metallography for Complex Microstructures Using Deep Learning: A Case Study in Ultrahigh Carbon Steel |
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