Title: Advances in electron backscatter diffraction Show Chinese title

Title: 电子背散射衍射的进步

Abstract: 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 orientation 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. Show Chinese abstract

Abstract: 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快速获得材料的微观结构见解。 我们包括使用AstroEBSD进行基于单像素的EBSD测绘和传统取向测绘；接着采用一种无监督机器学习方法，结合主成分分析和多变量统计以及改进的模板匹配方法，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，我们使用监督机器学习、迁移学习和卷积神经网络对相进行分类。