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Advanced Steel Microstructural Classification by Deep Learning Methods

Azimi, Seyedmajid and Britz, Dominik and Engstler, Michael and Fritz, Mario and Mücklich, Frank (2018) Advanced Steel Microstructural Classification by Deep Learning Methods. Nature, 8 (2128), pp. 1-14. Nature Publishing Group. DOI: 10.1038/s41598-018-20037-5 ISSN 0028-0836

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Official URL: https://www.nature.com/articles/s41598-018-20037-5

Abstract

The inner structure of a material is called microstructure. It stores the genesis of a material and determines all its physical and chemical properties. While microstructural characterization is widely spread and well known, the microstructural classification is mostly done manually by human experts, which gives rise to uncertainties due to subjectivity. Since the microstructure could be a combination of different phases or constituents with complex substructures its automatic classification is very challenging and only a few prior studies exist. Prior works focused on designed and engineered features by experts and classified microstructures separately from the feature extraction step. Recently, Deep Learning methods have shown strong performance in vision applications by learning the features from data together with the classification step. In this work, we propose a Deep Learning method for microstructural classification in the examples of certain microstructural constituents of low carbon steel. This novel method employs pixel-wise segmentation via Fully Convolutional Neural Network (FCNN) accompanied by a max-voting scheme. Our system achieves 93.94% classification accuracy, drastically outperforming the state-of-the-art method of 48.89% accuracy. Beyond the strong performance of our method, this line of research offers a more robust and first of all objective way for the difficult task of steel quality appreciation.

Item URL in elib:https://elib.dlr.de/124219/
Document Type:Article
Title:Advanced Steel Microstructural Classification by Deep Learning Methods
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Azimi, SeyedmajidSeyedmajid.Azimi (at) dlr.dehttps://orcid.org/0000-0002-6084-2272
Britz, DominikMaterial Engineering Center Saarland, Saarbrücken, Saarland UniversityUNSPECIFIED
Engstler, MichaelMaterial Engineering Center Saarland, Saarbrücken, Saarland UniversityUNSPECIFIED
Fritz, MarioMax Planck Institute for Informatics, Computer Vision and Multimodal Computing, SaarbrückenUNSPECIFIED
Mücklich, FrankMaterial Engineering Center Saarland, Saarbrücken, Saarland UniversityUNSPECIFIED
Date:February 2018
Journal or Publication Title:Nature
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:8
DOI :10.1038/s41598-018-20037-5
Page Range:pp. 1-14
Publisher:Nature Publishing Group
ISSN:0028-0836
Status:Published
Keywords:Deep Learning, microstructure, classification
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Zielske, Mandy
Deposited On:10 Dec 2018 10:55
Last Modified:10 Dec 2018 10:55

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