Revealing geometrically necessary dislocation density from electron backscatter patterns via multi-modal deep learning

The characterization of geometrically necessary dislocation (GND) is central to understanding the plastic deformation in materials. Currently, fast and accurate determination of GND density via Electron Backscatter Diffraction (EBSD) remains a challenge. Here, a multi-modal deep learning approach is proposed to predict GND density in terms of electron backscatter patterns (EBSPs) and dislocation configurations. The proposed multi modal architecture consists of two separated convolutional neural network (CNN) processing streams. One CNN stream aims at extracting pattern shifts from EBSPs, and the other CNN stream focuses on learning suitable representations of dislocation configurations. We also introduce a specific data augmentation strategy termed neighboring pairs generating strategy for the GND prediction task. Taking the GND density from dictionary indexing-based analysis as the target property, high accuracy is achieved on several aluminum samples. Also, our networks are robust to various forms of noise, and the prediction speed is as fast as modern EBSD scanning rates, enabling real-time GND density analysis possible.

Automated summary

A multi-modal deep learning approach is proposed to predict the geometrically necessary dislocation (GND) density in materials. The method utilizes electron backscatter patterns (EBSPs) and dislocation configurations to achieve accurate predictions. The study also introduces a specific data augmentation strategy for the GND prediction task. High accuracy is achieved on aluminum samples, and the networks are robust and capable of real-time analysis.