Rapid electron backscatter diffraction mapping: Painting by numbers

Abstract

Microstructure characterisation has been greatly enhanced through the use of electron backscatter diffraction (EBSD), where rich maps are generated through analysis of the crystal phase and orientation in the scanning electron microscope (SEM). Conventional EBSD analysis involves raster scanning of the electron beam and the serial analysis of each diffraction pattern in turn. For grain shape, crystallographic texture, and microstructure analysis this can be inefficient. In this work, we present Rapid EBSD, a data fusion approach combining forescatter electron (FSE) imaging with static sparse sampling of EBSD patterns. We segment the FSE image into regions of similar colour (i.e. phase and crystal orientation) and then collect representative EBSD data for each segmented region. This enables microstructural assessment to be performed at the spatial resolution of the (fast) FSE imaging whilst including orientation and phase information from EBSD analysis of representative points. We demonstrate the Rapid EBSD technique on samples of a cobalt based superalloy and a strained dual phase titanium alloy, comparing the results with conventional analysis. Rapid EBSD is advantageous for assessing grain size distributions in time-limited experiments.

TrueEBSD: Correcting spatial distortions in electron backscatter diffraction maps

“…FSE images can be acquired with the EBSD detector fully inserted ('near-field FSE') or partially retracted ('far-field FSE'). The far-field FSE image has strong electron channelling contrast [14,15], whereas the nearfield FSE image has weaker electron channelling contrast and more topography contrast.…”

“…To do this, we apply a series of transformations including the affine transformation [15], which has translation, scaling, rotation and shear degrees of freedom (Equation 1).…”

Efficient few-shot machine learning for classification of EBSD patterns

“…The collected diffraction patterns contain significant structural and chemical information and are similar to those collected in other techniques such as convergent beam electron diffraction (CBED) 44 , 45 . Despite the vast amount of information in the patterns, conventional EBSD has primarily focused on determining the three-dimensional orientation of individual grains in crystalline materials 43 , 46 – 48 . Furthermore, the commercial technique typically relies on Hough-based indexing with a look-up table of interplanar angles constructed from the set of selected reflectors for phases specified by the user 49 .…”

An Acquisition Parameter Study for Machine-Learning-Enabled Electron Backscatter Diffraction

“…EBSD is a scanning electron microscope (SEM)-based method that involves the capture of 2D diffraction patterns produced from an incident electron beam scattering, diffracting, and escaping from a well-polished “bulk” sample (Schwartz et al, 2009). Despite the vast amount of information in the patterns (Vecchio & Williams, 1987, 1988; Schwartz et al, 2009), conventional EBSD has primarily focused on determining three-dimensional orientation (Schwartz et al, 2009; Thomsen et al, 2013; Tong et al, 2019; Zhu et al, 2020 b ). Furthermore, the technique typically relies on a user-defined phase list and Hough-based indexing (Lassen, 1994).…”