Convolutional Neural Networks applied to EBSD maps to improve phase discriminations in steelsM. LAVRSKYI 1,2, *, N. GEY2,3, Ph. CHAROBERT4, N. LOUKACHENKO4, A. COUTURIER4, N. KOHOUT1, L. GERMAIN2,3
- Institut de Recherche Technologique Matériaux, Métallurgie et Procédés, 4 rue Augustin Fresnel F-57078, Metz, France
- Université de Lorraine, CNRS, Arts et Métiers Paris Tech, LEM3, F-57000 Metz, France
- Laboratory of Excellence on Design of Alloy Metals for Low-mAss Structures (DAMAS), Université de Lorraine, France
- INDUSTEEL (ArcelorMittal), Centre de Recherche des Matériaux du Creusot (CRMC), Le Creusot, France
The analysis of microstructures in multiphase steels is
highly complex but it is essential to achieve better process control or
to optimize their properties. EBSD is a well-suited technique for this
application since it provides much more information than classical
imaging. Even if martensite, bainite, and ferrite, have similar crystal
structures, they can be distinguished on EBSD maps by their local
misorientation and packets/blocks/sub-blocks arrangement. However, a
quantitative characterization of such complex steels microstructures
requires a considerable amount of time, effort and expertise.
Therefore, there is a need to develop a reliable tool to accelerate and
automates the phase recognition of multi-phase microstructures.
In previous studies, we have demonstrated the input of
Convolutional Neural Networks (CNNs) for this task [1,2]. The developed
CNN with the UNET architecture shows the ability to automatically
distinguish martensite, bainite, and ferrite in a low carbon industrial
steel with an accuracy of over 90%. The success of using artificial
neural networks is strongly dependent on enough and relevant database.
In this contribution, we assess the robustness of our model against the
variability of the input (steel grade, influence of sample preparation
and EBSD acquisition set-up). Additionally, we discuss the amount of
data needed to train an accurate model, including the contribution of
simulated EBSD microstructures and data augmentation.
 Martinez Ostormujof T., Purushottam Raj Purohit R., Breumier S.,
Gey N., Salib M., Germain L. (2021). Deep Learning for automated phase
segmentation in EBSD maps. A case study in Dual Phase steel
microstructures. Materials Characterization, 184:111638.
 Breumier S., Martinez Ostormujof T., B. Frincu, Gey N., P.E.
Aba-Perea, A. Couturier, N. Loukachenko, Germain L. (2022). Leveraging
EBSD data by deep learning for bainite, ferrite and martensite
segmentation. Materials Characterization, 186:111805.