skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Advanced Steel Microstructural Classification by Deep Learning Methods

Scientific reports, 2018-02, Vol.8 (1), p.2128-14, Article 2128 [Peer Reviewed Journal]

2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s) 2018 ;ISSN: 2045-2322 ;EISSN: 2045-2322 ;DOI: 10.1038/s41598-018-20037-5 ;PMID: 29391406

Full text available

Citations Cited by
  • Title:
    Advanced Steel Microstructural Classification by Deep Learning Methods
  • Author: Azimi, Seyed Majid ; Britz, Dominik ; Engstler, Michael ; Fritz, Mario ; Mücklich, Frank
  • Subjects: Classification ; Neural networks ; Segmentation ; Steel ; Teaching methods
  • Is Part Of: Scientific reports, 2018-02, Vol.8 (1), p.2128-14, Article 2128
  • Description: 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.
  • Publisher: England: Nature Publishing Group
  • Language: English
  • Identifier: ISSN: 2045-2322
    EISSN: 2045-2322
    DOI: 10.1038/s41598-018-20037-5
    PMID: 29391406
  • Source: Open Access: PubMed Central
    Directory of Open Access Journals
    ProQuest Academic
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait