Classification of the carbon-rich second phase in dual-phase steels

Carbon-rich second phase in dual phase steel

  • Sample of a multiphase steel with polygonal ferrite, bainite and carbon-rich second phases
  • Preparation and contrasting with modified Beraha etching
  • Images in the light microscope (LM) and scanning electron microscope (SEM)

Task definition:

  • Depending on the chemical composition and production parameters, the carbon-rich second phase in dual-phase steels can consist of pearlite, bainite or martensite
  • The bainitic microstructures can in turn be subdivided into various subclasses. As manual differentiation of these bainite subclasses is error-prone and difficult to reproduce, classification is realised using machine learning (ML)


  • Object-by-object classification using conventional ML. Scanning electron microscope (SEM) images are used. Image texture parameters and morphological parameters are used as distinguishing features between the microstructural components
  • Use of correlative microscopy (SEM and electron backscatter diffraction) for objective ground truth
  • 7 classes are taken into account: Pearlite, 5 different types of bainite and martensite

Carbon rich second phase in dual phase steels


  • The ML model achieves a classification accuracy of 82.9 %, which is an outstanding value considering the complexity of bainitic structures
  • The classes pearlite, martensite and upper and lower bainite can even be recognised with 90 % accuracy

Fields of application:

  • Automated, objective and reproducible microstructure classification
  • This new level of detail in microstructure classification enables improved quantification as the basis for new process-structure property correlations

Partners of cooperation:

  • Joint-stock company of Dillinger Huettenwerke, Dillingen/Saar


  • Müller, M., Britz, D., Staudt, T., & Mücklich, F. (2021). Microstructural Classification of Bainitic Subclasses in Low-Carbon Multi-Phase Steels Using Machine Learning Techniques. Metals, 1836(11).
  • Gola, J., Webel, J., Britz, D., Guitar, A., Staudt, T., Winter, M., & Mücklich, F. (2019). Objective microstructure classification by support vector machine (SVM) using a combination of morphological parameters and textural features for low carbon steels. Computational Materials Science, 160 (January), 186–196.
  • M. Azimi, D. Britz, M. Engstler, M. Fritz, F. Mücklich, Advanced steel microstructural classification by deep learning methods, Sci. Rep. 8 (2018) 1–14.


Dr.-Ing. Dominik Britz

Deputy Head MECS Saarbrücken

+49 681 302 70540

Adrian Thome, M.Sc.

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