  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.
