• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Artikel
  4. Using deep learning to classify steel materials objectively
 
  • Details
  • Full
Options
2023
Journal Article
Title

Using deep learning to classify steel materials objectively

Abstract
Up to now, the size of the microscopic crystallites has been assessed by metallographers by way of visual inspection- a subjective and error-prone method. Researchers at the Fraunhofer Institute for Mechanics of Materials IWM, in collaboration with Schaeffler Technologies AG & Co. KG, have developed a deep learning model that enables objective and automated assessment and determination of the grain size.
Author(s)
Durmaz, Ali Riza  
Fraunhofer-Institut für Werkstoffmechanik IWM  
Potu, Sai Teja
Leibniz University of Hannover, Institute of Mechanics and Computational Mechanics
Romich, Daniel
Schaeffler Technologies AG & Co. KG, Materials Technology
Möller, Johannes J.
Schaeffler Technologies AG & Co. KG, Materials Technology
Nütz, Ralf
Schaeffler Technologies AG & Co. KG, Materials Technology
Journal
Think.steel  
Language
English
Fraunhofer-Institut für Werkstoffmechanik IWM  
Keyword(s)
  • deep learning model

  • grain size determination

  • artificial Intelligence

  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024