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  4. Microstructure quality control of steels using deep learning
 
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2023
Journal Article
Title

Microstructure quality control of steels using deep learning

Abstract
In quality control, microstructures are investigated rigorously to ensure structural integrity, exclude the presence of critical volume defects, and validate the formation of the target microstructure. For quenched, hierarchically-structured steels, the morphology of the bainitic and martensitic microstructures are of major concern to guarantee the reliability of the material under service conditions. Therefore, industries conduct small sample-size inspections of materials cross-sections through metallographers to validate the needle morphology of such microstructures. We demonstrate round-robin test results revealing that this visual grading is afflicted by pronounced subjectivity despite the thorough training of personnel. Instead, we propose a deep learning image classification approach that distinguishes steels based on their microstructure type and classifies their needle length alluding to the ISO 643 grain size assessment standard. This classification approach facilitates the reliable, objective, and automated classification of hierarchically structured steels. Specifically, an accuracy of 96% and roughly 91% is attained for the distinction of martensite/bainite subtypes and needle length, respectively. This is achieved on an image dataset that contains significant variance and labeling noise as it is acquired over more than 10 years from multiple plants, alloys, etchant applications, and light optical microscopes by many metallographers (raters). Interpretability analysis gives insights into the decision-making of these models and allows for estimating their generalization capability.
Author(s)
Durmaz, Ali Riza  
Fraunhofer-Institut für Werkstoffmechanik IWM  
Potu, Sai Teja
Fraunhofer-Institut für Werkstoffmechanik IWM  
Romich, Daniel
Schaeffler Technologies AG & Co. KG, Schweinfurt
Möller, Johannes J.
Schaeffler Technologies AG & Co. KG, Schweinfurt
Nützel, Ralf
Schaeffler Technologies AG & Co. KG, Schweinfurt
Journal
Frontiers in Materials  
Open Access
DOI
10.3389/fmats.2023.1222456
Language
English
Fraunhofer-Institut für Werkstoffmechanik IWM  
Keyword(s)
  • bainite

  • deep learning

  • grain size

  • martensite

  • microstructure

  • quality control

  • steel

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