电子背散射衍射的研究进展
我们介绍了电子背散射衍射(EBSD)领域的一些最新进展。 我们强调如何使用开源算法和开放数据格式来快速开发材料的微观结构洞察力。 我们将 AstroEBSD 用于基于单像素的 EBSD mapping和常规的取向mapping; 其次是使用主成分分析和多变量统计结合精细模板匹配方法的无监督机器学习方法,以高精度快速索引取向数据。 接下来,我们将使用直接电子探测器捕获的衍射图案与动态模拟进行比较,并将其投影以创建高质量的实验“参考衍射球”。 最后,我们使用带有转移学习和卷积神经网络的监督机器学习对阶段进行分类。
Advances in electron backscatter diffraction
We present a few recent developments in the field of electron backscatter diffraction (EBSD). We highlight how open source algorithms and open data formats can be used to rapidly to develop microstructural insight of materials. We include use of AstroEBSD for single pixel based EBSD mapping and conventional mapping; followed by an unsupervised machine learning approach using principal component analysis and multivariate statistics combined with a refined template matching method to rapidly index orientation data with high precision. Next, we compare a diffraction pattern captured using direct electron detector with a dynamical simulation and project this to create a high quality experimental “reference diffraction sphere”. Finally, we classify phases using supervised machine learning with transfer learning and a convolutional neural network.
原文链接:
文中使用的直接电子探测器是选用的捷克Advacam公司的Minipix电子,离子、质子及X射线多功能探测器(点击了解详情)。