Segmentation of lath-shaped bainite in multiphase steels

Multiphase steel sample

Multiphase steel sample

  • Samples of a multiphase steel with polygonal ferrite, bainite and carbon-rich second phases
  • Preparation and contrasting with nital etching
  • Images were taken under a light microscope (LM) and scanning electron microscope (SEM)

Task definition:

  • Development of an segmentation routine for lath-shaped bainite


  • Use of Deep Learning (semantic segmentation)

  • Including ML models with data sets from light microscope and scanning electron microscope images

  • On the base of correlative microscopy (LM/SEM and electron backscatter diffraction) for objective ground truth


  • Very good model performance for LM and SEM images (pixel accuracy, intersection over union)

  • Low deviations of the phase components (1-2%)

  • Good segmentation even with new, unseen images


DL segmentation of lath-shaped bainite– LiMi

DL segmentation of lath-shaped bainite – SEM

Areas of application:

  • Automated, objective and reproducible recording of the lath-shaped bainite
  • Microstructure analysis as a basis for process-structure-property correlations

Partners for cooperation:

  • Fraunhofer Institut für Werkstoffmechanik, Freiburg
  • Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh
  • Aktien-Gesellschaft der Dillinger Hüttenwerke, Dillingen/Saar


  • A.R. Durmaz, M. Müller, B. Lei, A. Thomas, D. Britz, E.A. Holm, C. Eberl, F. Mücklich, P. Gumbsch, A deep learning approach for complex microstructure inference, Nat. Commun. 12 (2021) 1–15. Link to study


Dr.-Ing. Dominik Britz

Deputy Head MECS Saarbrücken

+49 681 302 70540

Adrian Thome, M.Sc.

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