電子背散射衍射的研究進(jìn)展  

我們介紹了電子背散射衍射（EBSD）領(lǐng)域的一些最新進(jìn)展。 我們強(qiáng)調(diào)如何使用開(kāi)源算法和開(kāi)放數(shù)據(jù)格式來(lái)快速開(kāi)發(fā)材料的微觀結(jié)構(gòu)洞察力。 我們將 AstroEBSD 用于基于單像素的 EBSD mapping和常規(guī)的取向mapping； 其次是使用主成分分析和多變量統(tǒng)計(jì)結(jié)合精細(xì)模板匹配方法的無(wú)監(jiān)督機(jī)器學(xué)習(xí)方法，以高精度快速索引取向數(shù)據(jù)。 接下來(lái)，我們將使用直接電子探測(cè)器捕獲的衍射圖案與動(dòng)態(tài)模擬進(jìn)行比較，并將其投影以創(chuàng)建高質(zhì)量的實(shí)驗(yàn)“參考衍射球”。 最后，我們使用帶有轉(zhuǎn)移學(xué)習(xí)和卷積神經(jīng)網(wǎng)絡(luò)的監(jiān)督機(jī)器學(xué)習(xí)對(duì)階段進(jìn)行分類(lèi)。

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.

原文鏈接：

Foden, Alex, Alessandro Previero, and Thomas Benjamin Britton. "Advances in electron backscatter diffraction." arXiv preprint arXiv:1908.04860 (2019).

文中使用的直接電子探測(cè)器是選用的捷克Advacam公司的Minipix電子，離子、質(zhì)子及X射線多功能探測(cè)器（點(diǎn)擊了解詳情）。