An Acquisition Parameter Study for Machine-Learning-Enabled Electron Backscatter Diffraction

Abstract

Abstract

Leveraging EBSD data by deep learning for bainite, ferrite and martensite segmentation

“…As revealed by table 5, the model does not seem to be sensitive to the acquisition conditions, provided that they are sufficiently good to evidence a contrast between the different phases on the model input. Machine learning model robustness over different acquisition conditions was scarcely addressed in the literature for microstructures segmentation [11,14,27]. Such approach was however not compatible with the U-Net segmentation model used in this study, as it is mostly composed of convolutional layers.…”

“…The ability of the model to generalize over different acquisition conditions (acquisition step, diffraction pattern acquisition parameters) was also addressed. Indeed, it is necessary to ensure that the developed model will work consistently whatever the EBSD/SEM setup for optimized data acquisition [11,14,27].…”