在扫描电子显微镜中利用相关电子衍射和X射线光谱的统计数据进行先进的表征
Advancing characterisation with statistics from correlative electron diffraction and X-ray spectroscopy, in the scanning electron microscope
摘要：
微结构中微量相的常规和独特测定对材料科学至关重要。仅在冶金学方面，应用包括合金和工艺开发以及对服务退化的理解。我们开发了一种相关的方法，探索高温合金的微观结构，在扫描电子显微镜（SEM）检查，同时使用能量色散X射线光谱（EDS）和电子背散射衍射（EBSD）。这是在适当的长度尺度下进行的，用于表征碳化物相的形状、尺寸、位置和分布。EDS和EBSD数据是使用两种不同的物理过程产生的，但每一种都提供了与入射电子束相互作用的材料的特征。在“大数据”方法的推动下，后处理方面的最新进展包括使用主成分分析。随后对组件进行表征，以将标签分配给映射区域。为了提供物理上有意义的信号，可以旋转主分量以控制方差的分布。在这项工作中，我们进一步发展这种方法通过加权PCA方法。我们同时使用EDS和EBSD信号，从而使用EDS（化学）和EBSD（晶体结构）信息标记每个区域。这提供了一种放大映射区域中非常小的相位的信噪比的新方法，特别是在EDS或EBSD信号对于分类来说不够独特的情况下。
英文摘要：
The routine and unique determination of minor phases in microstructures is critical to materials science. In metallurgy alone, applications include alloy and process development and the understanding of degradation in service. We develop a correlative method, exploring superalloy microstructures which are examined in the scanning electron microscope (SEM) using simultaneous energy dispersive X-ray spectroscopy (EDS) and electron backscatter diffraction (EBSD). This is performed at an appropriate length scale for characterisation of carbide phases' shape, size, location, and distribution. EDS and EBSD data are generated using two different physical processes, but each provide a signature of the material interacting with the incoming electron beam. Recent advances in post-processing, driven by "big data" approaches, include use of principal component analysis (PCA). Components are subsequently characterised to assign labels to a mapped region. To provide physically meaningful signals, the principal components may be rotated to control the distribution of variance. In this work, we develop this method further through a weighted PCA approach. We use the EDS and EBSD signals concurrently, thereby labelling each region using both EDS (chemistry) and EBSD (crystal structure) information. This provides a new method of amplifying signal-to-noise for very small phases in mapped regions, especially where the EDS or EBSD signal is not unique enough alone for classification.
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