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      Advanced Steel Microstructural Classification by Deep Learning Methods

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          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.

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          Most cited references 15

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                Author and article information

                Contributors
                seyedmajid.azimi@dlr.de
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                1 February 2018
                1 February 2018
                2018
                : 8
                Affiliations
                [1 ]Max Planck Institute for Informatics, Computer Vision and Multimodal Computing, Saarbrücken, Germany
                [2 ]Material Engineering Center Saarland, Saarbrücken, Germany
                [3 ]ISNI 0000 0001 2167 7588, GRID grid.11749.3a, Saarland University, Chair of Functional Materials, ; Saarbrücken, Germany
                [4 ]ISNI 0000 0000 8983 7915, GRID grid.7551.6, Present Address: German Aerospace Center (DLR), Remote Sensing Technology Institute, ; Weßling, Germany
                Article
                20037
                10.1038/s41598-018-20037-5
                5794926
                29391406
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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