Segmentation of prior austenite grains in steel

Task definition:

  • Determining the prior austenite grain size of a steel has been a long-standing challenge. Established approaches (e.g. chemical etching with Bechet-Beaujard or crystallographic reconstruction using electron backscatter diffraction (EBSD)) have limitations specific to their processes
  • A Deep Learning (DL) model is to be trained to robustly segment the prior austenite grains (PAG) in microstructures after Bechet-Beaujard etching, despite the occurance of etching artefacts
  • Additionally, another DL model is to be trained to segment the PAG in microstructures etched with regular nital under specific conditions


  • Use of DL (semantic segmentation)
  • Training separate models for Bechet-Beaujard and Nital etching
  • Use of correlative microscopy (LM/SEM and EBSD) for objective ground truth

Segmentation example for austenite grains


  • Very good model performance for both Bechet-Beaujard and Nital etching
  • Good segmentation even with new, unseen images

Segmentation of Bechet-Beaujard images

Segmentation of Nital images

Fields of application:

  • Automated, objective and reproducible determination of the prior austenite grain size
  • Unlike the often-manual evaluation methods (e.g., comparative grain size according to the line cut method), this approach enables determination of the grain size distribution

  • Increased efficiency: Quick quantification of even larger sample areas (e.g. 2 mm x 2 mm)
  • Time saving potential by substituting  EBSD reconstruction
  • Improved microstructural analysis as a basis for process-structure property correlations

Partners of cooperation:

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


  • Bachmann, B. I., Müller, M., Britz, D., Durmaz, A. R., Ackermann, M., Shchyglo, O., Staudt, T., & Mücklich, F. (2022). Efficient reconstruction of prior austenite grains in steel from etched light optical micrographs using deep learning and annotations from correlative microscopy. Frontiers in Materials, 9(October), 1–18.
  • Laub, M., Bachmann, B.-I., Detemple, E., Scherff, F., Staudt, T., Müller, M., Britz, D., Mücklich, F., & Motz, C. (2022). Determination of grain size distribution of prior austenite grains through a combination of a modified contrasting method and machine learning. Practical Metallography, 60(1), 4–36.



Dr.-Ing. Dominik Britz

Deputy head of MECS Saarbrücken

+49 681 302 70540

Adrian Thome, M.Sc.

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