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
Realization
- 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 (LOM, SEM and electron backscatter diffraction) for objective ground truth
Results
- 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
Cooperation partners
- Fraunhofer Institut für Werkstoffmechanik, Freiburg
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh
- Aktien-Gesellschaft der Dillinger Hüttenwerke, Dillingen/Saar
References
- 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
Contact for questions
Dr.-Ing. Dominik Britz
Deputy Head MECS Saarbrücken
Dr.-Ing. Tobias Fox
Chief Operating Officer
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