Lu Qi, Wu Jiayi, Liu Shilong, Zhang Shiqi, Cai Xiaorong, Li Wei, Jiang Jun, Jin Xuejun
Shanghai Key Laboratory of Materials Laser Processing and Modification, Shanghai Jiao Tong University, Shanghai 200240, PR China; Institute of Advanced Steels and Materials, School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, PR China.
Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an 710049, China.
Ultramicroscopy. 2022 Jul;237:113519. doi: 10.1016/j.ultramic.2022.113519. Epub 2022 Mar 29.
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.
几何必要位错（GND）的表征是理解材料塑性变形的核心。目前，通过电子背散射衍射（EBSD）快速准确地测定GND密度仍然是一个挑战。在此，提出了一种多模态深度学习方法，以根据电子背散射图案（EBSP）和位错构型来预测GND密度。所提出的多模态架构由两个分离的卷积神经网络（CNN）处理流组成。一个CNN流旨在从EBSP中提取图案偏移，另一个CNN流专注于学习位错构型的合适表示。我们还针对GND预测任务引入了一种特定的数据增强策略，称为相邻对生成策略。以基于字典索引分析的GND密度作为目标属性，在几个铝样品上实现了高精度。此外，我们的网络对各种形式的噪声具有鲁棒性，并且预测速度与现代EBSD扫描速率一样快，使得实时GND密度分析成为可能。
