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

TL;DR

This study demonstrates that convolutional neural networks, specifically U-Net, can accurately and efficiently classify phases in EBSD maps of steel microstructures, outperforming existing methods.

Contribution

The paper introduces a CNN-based approach using U-Net for automatic phase segmentation in EBSD data, achieving higher accuracy and faster results than previous methods.

Findings

Pixel-wise accuracy of ~95% with raw data

Accuracy of ~98% with orientation-derived parameters

Faster processing compared to existing approaches

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.