Q-RBSA: High-Resolution 3D EBSD Map Generation Using An Efficient Quaternion Transformer Network
This addresses the bottleneck in crystallographic data collection for materials science, offering a more efficient method for generating 3D EBSD maps, though it appears incremental as it builds on existing deep learning and physics-based approaches.
The paper tackles the problem of time-consuming and expensive 3D material microstructural data acquisition by proposing a deep learning framework that generates high-resolution 3D EBSD maps from sparse sections, showing it can predict missing samples compared to ground truth in titanium alloys.
Gathering 3D material microstructural information is time-consuming, expensive, and energy-intensive. Acquisition of 3D data has been accelerated by developments in serial sectioning instrument capabilities; however, for crystallographic information, the electron backscatter diffraction (EBSD) imaging modality remains rate limiting. We propose a physics-based efficient deep learning framework to reduce the time and cost of collecting 3D EBSD maps. Our framework uses a quaternion residual block self-attention network (QRBSA) to generate high-resolution 3D EBSD maps from sparsely sectioned EBSD maps. In QRBSA, quaternion-valued convolution effectively learns local relations in orientation space, while self-attention in the quaternion domain captures long-range correlations. We apply our framework to 3D data collected from commercially relevant titanium alloys, showing both qualitatively and quantitatively that our method can predict missing samples (EBSD information between sparsely sectioned mapping points) as compared to high-resolution ground truth 3D EBSD maps.