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

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. • Deep Learning Convolutional Neural Networks were successfully applied to EBSD maps. • Beyond 98% accuracy to discriminate Ferrite from Martensite in a Dual Phase Steel • Misidentifications occurred in Widmanstätten ferrite and autotempered martensite. • Relatively few labeled EBSD maps are required for training.