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)
Realization
- A travelling window splits the micrograph into individual sections, which are then classified by a Deep Learning (DL) model
- Training of separate models for LOM and SEM images
- Use of correlative microscopy (LOM, 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)
Results
- The models for sectional classification achieve accuracies of 93.5% for SEM images and 89.8% for LOM images. Considering the complexity of the microstructure and the limited LOM resolution, these are outstanding values
- The sliding window approach also enables good segmentation of entire unseen images
LM /SEM 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 LOM and the required resolutions
Cooperation partners
- Aktien-Gesellschaft der Dillinger Hüttenwerke, Dillingen/Saar
References
- 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/10.3390/met13081395
Contact for questions
Dr.-Ing. Dominik Britz
Deputy Head MECS Saarbrücken
Dr.-Ing. Tobias Fox
Chief Operating Officer
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