Density-based clustering of crystal (mis)orientations and the <i>orix</i> Python library

Johnstone, Duncan N.; Martineau, Baudouin; Crout, Phillip; Midgley, Paul A.; Eggeman, Alexander S.

doi:10.1107/s1600576720011103

Cited by 29 publications

(16 citation statements)

References 22 publications

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“…Several approaches may be considered to detect isolated clusters considering local orientation density to overcome the influence of outlying points and noise. Density-based clustering using the DBSCAN algorithm was shown to provide satisfactory results for relatively small sets of orientations (Johnstone et al, 2020). In the present case, large EBSD sets (up to four million points) prevent computing the crystallographic distance matrix for all orientations in a decent computation time.…”

Section: Generation Of Parent Phase From Child Phasementioning

confidence: 79%

Insights into a dual-phase steel microstructure using EBSD and image-processing-based workflow

Mollens

Roux²,

Hild³

et al. 2022

J Appl Cryst

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Quantitative metallography to understand the morphology of different crystallographic phases in a material often rests on the segmentation and classification of electron backscatter diffraction (EBSD) maps. Image analysis offers rich toolboxes to perform such tasks based on `scalar' images. Embracing the entire wealth of information provided by crystallography, operations such as erosion, dilation, interpolation, smoothing and segmentation require generalizations to do justice to the very nature of crystal orientations (e.g. preserving properties like frame indifference). The present study gives such extensions based on quaternion representation of crystal orientations. A dual-phase stainless steel specimen is used to illustrate the different steps of such a procedure.

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Section: Generation Of Parent Phase From Child Phasementioning

confidence: 79%

Insights into a dual-phase steel microstructure using EBSD and image-processing-based workflow

Mollens

Roux²,

Hild³

et al. 2022

J Appl Cryst

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“…Several open-source packages have been developed to analyze data sets, , such as py4DSTEM and pyXem . First steps in the analysis include aligning all diffraction frames, subtracting a background, and correcting for ellipticity introduced by stigmation in objective or projector lenses.…”

Section: Discussionmentioning

confidence: 99%

“…Several open-source packages have been developed to analyze data sets, 27,36 such as py4DSTEM 32 and pyXem. 37 First steps in the analysis include aligning all diffraction frames, subtracting a background, and correcting for ellipticity introduced by stigmation in objective or projector lenses. Then, a crosscorrelation is performed against a template of the probe-forming aperture to identify the positions of Bragg reflections.…”

Section: ■ Data Analysismentioning

confidence: 99%

4D-STEM of Beam-Sensitive Materials

et al. 2021

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Scanning electron nanobeam diffraction, or 4D-STEM (four-dimensional scanning transmission electron microscopy), is a flexible and powerful approach to elucidate structure from "soft" materials that are challenging to image in the transmission electron microscope because their structure is easily damaged by the electron beam. In a 4D-STEM experiment, a converged electron beam is scanned across the sample and a pixelated camera records a diffraction pattern at each scan position. This four-dimensional dataset can be mined for various analyses, producing maps of local crystal orientation, structural distortions, crystallinity, or different structural classes.Holding the sample at cryogenic temperatures minimizes diffusion of radicals and the resulting damage and disorder caused by the electron beam. The total fluence of incident electrons can easily be controlled during 4D-STEM experiments by careful use of the beam blanker, steering of the localized electron dose, and by minimizing the fluence in the convergent beam thus minimizing beam damage. This technique can be applied to both organic and inorganic materials that are known to be beam-sensitive; they can be highly crystalline, semi-crystalline, mixed phase, or amorphous (examples are shown in Figure 1). One common example is the case for many organic materials that have a - stacking of polymer chains or rings on the order of 3.4-4.2 Å separation.If these chains or rings are aligned in some regions, they will produce distinct diffraction spots (as would other crystalline spacings in this range), though they may be weak or diffuse for disordered or weakly-scattering materials. We can reconstruct the orientation of the - stacking, the degree of - stacking in the sample, and the domain size of the aligned regions. This account summarizes illumination conditions and experimental parameters for 4D-STEM experiments with the goal of producing images of structural features for materials that are beam-sensitive. We will discuss experimental parameters including sample cooling, probe size and shape, fluence, and cameras. 4D-STEM has been applied to a variety of materials, not only as an advanced technique for model systems, but as a technique for the beginning microscopist to answer materials science questions. It is noteworthy that the experimental data acquisition does not require an aberrationcorrected TEM, but can be produced on a variety of instruments with the right attention to experimental parameters.

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“…Simulated patterns in the dictionary are projected from the master pattern onto the EBSD detector using an average PC per dataset, found from the Al calibration patterns using Hough indexing in the Python package PyEBSDIndex [22]. The orientations of the simulated patterns are sampled using cubochoric sampling [23] as implemented in the Python package orix v0.7 [24,25]. The orientations are uniformly distributed in the fundamental zone with an average misorientation angle of 1.4°, resulting in a dictionary of about 300 000 simulated patterns.…”

Section: Analysis Of Ebsd Patterns and Orientationsmentioning

confidence: 99%

Correlated subgrain and particle analysis of a recovered Al-Mn alloy by directly combining EBSD and backscatter electron imaging

Ånes¹,

Helvoort²,

Marthinsen³

2022

Preprint

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Correlated analysis of (sub)grains and particles in alloys is important to understand transformation processes and control material properties. A multimodal data fusion workflow directly combining subgrain data from electron backscatter diffraction (EBSD) and particle data from backscatter electron (BSE) images in the scanning electron microscope is presented. The BSE images provide detection of particles smaller than the applied step size of EBSD down to 0.03 µm in diameter. The workflow is demonstrated on a cold-rolled and recovered Al-Mn alloy, where constituent particles formed during casting and dispersoids formed during subsequent heating affect recovery and recrystallization upon annealing. The multimodal dataset enables statistical analysis including subgrains surrounding constituent particles and dispersoids' location with respect to subgrain boundaries. Among the subgrains of recrystallization texture, Cube{001} 100 subgrains experience an increased Smith-Zener drag from dispersoids on their boundaries compared to CubeND{001} 310 and P{011} 56 6 subgrains, with the latter experiencing the lowest drag. Subgrains at constituent particles are observed to have a growth advantage due to a lower dislocation density and higher boundary misorientation angle. The dispersoid size per subgrain boundary length increases as a function of misorientation angle. The workflow should be applicable to other alloy systems where there is a need for analysis correlating grains and grain boundaries with secondary phases smaller than the applied EBSD step size but resolvable by BSE imaging.

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Density-based clustering of crystal (mis)orientations and the orix Python library

Cited by 29 publications

References 22 publications

Insights into a dual-phase steel microstructure using EBSD and image-processing-based workflow

Insights into a dual-phase steel microstructure using EBSD and image-processing-based workflow

4D-STEM of Beam-Sensitive Materials

Correlated subgrain and particle analysis of a recovered Al-Mn alloy by directly combining EBSD and backscatter electron imaging

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