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Journal Articles Materials Characterization Year : 2021

Deep Learning for automated phase segmentation in EBSD maps. A case study in Dual Phase steel microstructures

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Abstract

Electron Backscattering Diffraction (EBSD) provides important information to discriminate phase transformation products in steels. This task is conventionally performed by an expert, who carries a high degree of subjectivity and requires time and effort. In this paper, we question if Convolutional Neural Networks (CNNs) are able to extract meaningful features from EBSD-based data in order to automatically classify the present phases within a steel microstructure. The selected case of study is ferrite-martensite discrimination and U-Net has been selected as the network architecture to work with. Pixel-wise accuracies around ~95% have been obtained when inputting raw orientation data, while ~98% has been reached with orientation-derived parameters such as Kernel Average Misorientation (KAM) or pattern quality. Compared to other available approaches in the literature for phase discrimination, the models presented here provided higher accuracies in shorter times. These promising results open a possibility to work on more complex steel microstructures.
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Dates and versions

hal-03450484 , version 1 (26-11-2021)
hal-03450484 , version 2 (14-12-2021)

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Cite

T Martinez Ostormujof, Rrp Purushottam Raj Purohit, S Breumier, Nathalie Gey, M Salib, et al.. Deep Learning for automated phase segmentation in EBSD maps. A case study in Dual Phase steel microstructures. Materials Characterization, 2021, 184, pp.111638. ⟨10.1016/j.matchar.2021.111638⟩. ⟨hal-03450484v2⟩
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