@article {1686, title = {Electron backscattered diffraction using a new monolithic direct detector: High resolution and fast acquisition}, journal = {Ultramicroscopy}, volume = {220}, year = {2021}, pages = {113160}, abstract = {

A monolithic active pixel sensor based direct detector that is optimized for the primary beam energies in scanning electron microscopes is implemented for electron back-scattered diffraction (EBSD) applications. The high detection efficiency of the detector and its large array of pixels allow sensitive and accurate detection of Kikuchi bands arising from primary electron beam excitation energies of 4 keV to 28 keV, with the optimal contrast occurring in the range of 8\–16 keV. The diffraction pattern acquisition speed is substantially improved via a sparse sampling mode, resulting from the acquisition of a reduced number of pixels on the detector. Standard inpainting algorithms are implemented to effectively estimate the information in the skipped regions in the acquired diffraction pattern. For EBSD mapping, an acquisition speed as high as 5988 scan points per second is demonstrated, with a tolerable fraction of indexed points and accuracy. The collective capabilities spanning from high angular resolution EBSD patterns to high speed pattern acquisition are achieved on the same detector, facilitating simultaneous detection modalities that enable a multitude of advanced EBSD applications, including lattice strain mapping, structural refinement, low-dose characterization, 3D-EBSD and dynamic in situ EBSD.

}, keywords = {Direct detection, EBSD, Indexing, Inpainting, SEM, Sparse sampling}, issn = {18792723}, doi = {10.1016/j.ultramic.2020.113160}, url = {https://doi.org/10.1016/j.ultramic.2020.113160}, author = {Wang, Fulin and Echlin, Mclean P. and Taylor, Aidan A. and Shin, Jungho and Bammes, Benjamin and Levin, Barnaby D.A. and De Graef, Marc and Pollock, Tresa M. and Gianola, Daniel S.} } @article {1366, title = {Accurate reconstruction of EBSD datasets by a multimodal data approach using an evolutionary algorithm}, journal = {Materials Characterization}, volume = {150}, year = {2019}, pages = {184{\textendash}198}, abstract = {

A new method has been developed for the correction of the distortions and/or enhanced phase differentiation in Electron Backscatter Diffraction (EBSD) data. Using a multi-modal data approach, the method uses segmented images of the phase of interest (laths, precipitates, voids, inclusions) on images gathered by backscattered or secondary electrons of the same area as the EBSD map. The proposed approach then searches for the best transformation to correct their relative distortions and recombines the data in a new EBSD file. Speckles of the features of interest are first segmented in both the EBSD and image data modes. The speckle extracted from the EBSD data is then meshed, and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is implemented to distort the mesh until the speckles superimpose. The quality of the matching is quantified via a score that is linked to the number of overlapping pixels in the speckles. The locations of the points of the distorted mesh are compared to those of the initial positions to create pairs of matching points that are used to calculate the polynomial function that describes the distortion the best. This function is then applied to un-distort the EBSD data, and the phase information is inferred using the data of the segmented speckle. Fast and versatile, this method does not require any human annotation and can be applied to large datasets and wide areas. Besides, this method requires very few assumptions concerning the shape of the distortion function. It can be used for the single compensation of the distortions or combined with the phase differentiation. The accuracy of this method is of the order of the pixel size. Some application examples in multiphase materials with feature sizes down to 1 \μm are presented, including Ti-6Al-4 V Titanium alloy, Rene 65 and additive manufactured Inconel 718 Nickel-base superalloys.

}, keywords = {CMA-ES, Distortions, EBSD, Image segmentation, Multi-modal data}, issn = {10445803}, doi = {10.1016/j.matchar.2019.01.033}, url = {https://doi.org/10.1016/j.matchar.2019.01.033}, author = {Charpagne, Marie Agathe and Strub, Florian and Pollock, Tresa M.} } @article {1471, title = {Application of chord length distributions and principal component analysis for quantification and representation of diverse polycrystalline microstructures}, journal = {Materials Characterization}, volume = {145}, year = {2018}, pages = {671{\textendash}685}, abstract = {

Quantification of mesoscale microstructures of polycrystalline materials is important for a range of practical tasks of materials design and development. The current protocols of quantifying grain size and morphology often rely on microstructure metrics (e.g., mean grain diameter) that overlook important details of the mesostructure. In this work, we present a quantification framework based on directionally resolved chord length distribution and principal component analysis as a means of extracting additional information from 2-D microstructural maps. Towards this end, we first present in detail a method for calculating chord length distribution based on boundary segments available in modern digital datasets (e.g., from microscopy post-processing) and their low-rank representations by principal component analysis. The utility of the proposed framework for capturing grain size, morphology, and their anisotropy for efficient visualization, representation, and specification of polycrystalline microstructures is then demonstrated in case studies on datasets from synthetic generation, experiments (on Ni-base superalloys), and simulations (on steel during recrystallization).

}, keywords = {Chord length distribution, EBSD, Grain size, Microstructure, Principal component analysis}, issn = {10445803}, doi = {10.1016/j.matchar.2018.09.020}, url = {https://doi.org/10.1016/j.matchar.2018.09.020}, author = {Latypov, Marat I. and K{\"u}hbach, Markus and Beyerlein, Irene J. and Stinville, Jean Charles and Toth, Laszlo S. and Pollock, Tresa M. and Kalidindi, Surya R.} }