Microstructure classification in heat-treatable steels

Task definition:

  • The differentiation between bainite and martensite in tempered structures is still often carried out manually and purely qualitatively
  • A purely light microscopic evaluation and in particular a differentiation between martensite, (self-)tempered martensite and lower bainite, which can all occur simultaneously, is highly subjective and error-prone
  • Instead, a classification is to be realised using Machine Learning (ML)


  • A travelling window splits the micrograph into individual sections, which are then classified by a Deep Learning (DL) model
  • Training of separate models for LM and SEM images
  • Use of correlative microscopy (LM/SEM and electron backscatter diffraction) for objective ground truth
  • In this application example, 4 classes are considered: (a) Martensite, (b) (self-) tempered martensite, (c) lower bainite, and (d) upper bainite

4 classes for LM/SEM and electron backscatter diffraction, from Bachmann et al., (2023)


  • The models for sectional classification achieve accuracies of 93.5 % for SEM images and 89.8 % for LM images. Considering the complexity of the microstructure and the limited LM resolution, these are outstanding values
  • The travelling window approach also enables good segmentation of entire unseen images

LM /REM recording from Bachmann et al., (2023)

Areas 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
  • The correlative data enables a systematic investigation of the application limits of LM and the required resolutions

Partners for cooperation:

  • Aktien-Gesellschaft der Dillinger Hüttenwerke, Dillingen/Saar


  • Bachmann, B., Müller, M., Britz, D., Staudt, T., & Mücklich, F. (2023). Reproducible Quantification of the Microstructure of Complex Quenched and Quenched and Tempered Steels Using Modern Methods of Machine Learning. Metals, 13(1395), 1–27. https://doi.org/https://doi.org/10.3390/met13081395


Dr.-Ing. Dominik Britz

Deputy Head MECS Saarbrücken

+49 681 302 70540

Adrian Thome, M.Sc.

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