Electron backscatter diffraction (EBSD) is a powerful tool for determining the orientations of near-surface grains in engineering materials. However, many ceramics present challenges for routine EBSD data collection and indexing due to small grain sizes, high crack densities, beam and charge sensitivities, low crystal symmetries, and pseudo-symmetric pattern variants. Micro-cracked monoclinic hafnia, tetragonal hafnon, and hafnia/hafnon composites exhibit all such features, and are used in the present work to show the efficacy of a novel workflow based on a direct detecting EBSD sensor and a state-of-the-art pattern indexing approach. At 5 and 10 keV primary beam energies (where beam-induced damage and surface charge accumulation are minimal), the direct electron detector produces superior diffraction patterns with 10x lower doses compared to a phosphor-coupled indirect detector. Further, pseudo-symmetric variant-related indexing errors from a Hough-based approach (which account for at least 4%-14% of map areas) are easily resolved by dictionary indexing. In short, the workflow unlocks fundamentally new opportunities to characterize materials historically unsuited for EBSD.
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Phase determination in dual phase steels via HREBSD‐based tetragonality mapping
Electron Backscatter Diffraction (EBSD) is a widely used approach for characterising the microstructure of various materials. However, it is difficult to accurately distinguish similar (body centred cubic and body centred tetragonal, with small tetragonality) phases in steels using standard EBSD software. One method to tackle the problem of phase distinction is to measure the tetragonality of the phases, which can be done using simulated patterns and cross‐correlation techniques to detect distortion away from a perfectly cubic crystal lattice. However, small errors in the determination of microscope geometry (the so‐called pattern or projection centre) can cause significant errors in tetragonality measurement and lead to erroneous results. This paper utilises a new approach for accurate pattern centre determination via a strain minimisation routine across a large number of grains in dual phase steels. Tetragonality maps are then produced and used to identify phase and estimate local carbon content. The technique is implemented using both kinetically simulated and dynamically simulated patterns to determine their relative accuracy. Tetragonality maps, and subsequent phase maps, based on dynamically simulated patterns in a point‐by‐point and grain average comparison are found to consistently produce more precise and accurate results, with close to 90% accuracy for grain phase identification, when compared with an image‐quality identification method. The error in tetragonality measurements appears to be of the order of 1%, thus producing a commensurate ∼0.2% error in carbon content estimation. Such an error makes the technique unsuitable for estimation of total carbon content of most commercial steels, which often have carbon levels below 0.1%. However, even in the DP steel for this study (0.1 wt.% carbon) it can be used to map carbon in regions with higher accumulation (such as in martensite with nonhomogeneous carbon content). Lay DescriptionElectron Backscatter Diffraction (EBSD) is a widely used approach for characterising the microstructure of various materials. However, it is difficult to accurately distinguish similar (BCC and BCT) phases in steels using standard EBSD software due to the small difference in crystal structure. One method to tackle the problem of phase distinction is to measure the tetragonality, or apparent ‘strain’ in the crystal lattice, of the phases. This can be done by comparing experimental EBSD patterns with simulated patterns via cross‐correlation techniques, to detect distortion away from a perfectly cubic crystal lattice. However, small errors in the determination of microscope geometry (the so‐called pattern or projection centre) can cause significant errors in tetragonality measurement and lead to erroneous results. This paper utilises a new approach for accurate pattern centre determination via a strain minimisation routine across a large number of grains in dual phase steels. Tetragonality maps are then produced and used to identify phase and estimate local carbon content. The technique is implemented using both simple kinetically simulated and more complex dynamically simulated patterns to determine their relative accuracy. Tetragonality maps, and subsequent phase maps, based on dynamically simulated patterns in a point‐by‐point and grain average comparison are found to consistently produce more precise and accurate results, with close to 90% accuracy for grain phase identification, when compared with an image‐quality identification method. The error in tetragonality measurements appears to be of the order of 1%, thus producing a commensurate error in carbon content estimation. Such an error makes an estimate of total carbon content particularly unsuitable for low carbon steels; although maps of local carbon content may still be revealing.Application of the method developed in this paper will lead to better understanding of the complex microstructures of steels, and the potential to design microstructures that deliver higher strength and ductility for common applications, such as vehicle components.
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- Award ID(s):
- 1926662
- PAR ID:
- 10488287
- Publisher / Repository:
- Royal Microscopy Society
- Date Published:
- Journal Name:
- Journal of Microscopy
- Volume:
- 282
- Issue:
- 1
- ISSN:
- 0022-2720
- Page Range / eLocation ID:
- 60 to 72
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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A ubiquitous structural feature in biological systems is texture in extracellular matrix that gains functions when hardened, for example, cell walls, insect scales, and diatom tests. Here, we develop patterned liquid crystal elastomer (LCE) particles by recapitulating the biophysical patterning mechanism that forms pollen grain surfaces. In pollen grains, a phase separation of extracellular material into a pattern of condensed and fluid-like phases induces undulations in the underlying elastic cell membrane to form patterns on the cell surface. In this work, LCE particles with variable surface patterns were created through a phase separation of liquid crystal oligomers (LCOs) droplet coupled to homeotropic anchoring at the droplet interface, analogously to the pollen grain wall formation. Specifically, nematically ordered polydisperse LCOs and isotropic organic solvent (dichloromethane) phase-separate at the surface of oil-in-water droplets, while, different LCO chain lengths segregate to different surface curvatures simultaneously. This phase separation, which creates a distortion in the director field, is in competition with homeotropic anchoring induced by sodium dodecyl sulfate (SDS). By tuning the polymer chemistry of the system, we are able to influence this separation process and tune the types of surface patterns in these pollen-like microparticles. Our study reveals that the energetically favorable biological mechanism can be leveraged to offer simple yet versatile approaches to synthesize microparticles for mechanosensing, tissue engineering, drug delivery, energy storage, and displays.more » « less
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Abstract To study the microstructural evolution of polymineralic rocks, we performed deformation experiments on two‐phase aggregates of olivine (Ol) + ferropericlase (Per) with periclase fractions (fPer) between 0.1 and 0.8. Additionally, single‐phase samples of both Ol and Per were deformed under the same experimental conditions to facilitate comparison of the microstructures in two‐phase and single‐phase materials. Each sample was deformed in torsion atT = 1523 K,P = 300 MPa at a constant strain rate up to a final shear strain of γ = 6 to 7. Microstructural developments, analyzed via electron backscatter diffraction (EBSD), indicate differences in both grain size and crystalline texture between single‐ and two‐phase samples. During deformation, grain size approximately doubled in our single‐phase samples of Ol and Per but remained unchanged or decreased in two‐phase samples. Zener‐pinning relationships fit to the mean grain sizes in each phase for samples with 0.1 ≤ fPer≤ 0.5 and for those with 0.8 ≥ fPer ≥ 0.5 demonstrate that the grain size of the primary phase is controlled by phase‐boundary pinning. Crystallographic preferred orientations, determined for both phases from EBSD data, are significantly weaker in the two‐phase materials than in the single‐phase materials.more » « less
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{"Abstract":["This dataset provides high-resolution Kikuchi diffraction patterns and\n associated orientation mapping data collected from both wrought and\n as-built additively manufactured (AM) Inconel 718 superalloys. The dataset\n includes raw electron backscatter diffraction (EBSD) patterns stored as\n .tif images and organized through .up2 metadata files, along with\n processed orientation data in .ang format. These measurements were\n acquired using a high-sensitivity EBSD detector over large scan areas,\n enabling detailed spatial resolution of microstructural features such as\n grain orientations, subgrain boundaries, and processing-induced texture.\n The dataset supports a range of applications, including machine learning\n for pattern recognition and the development of robust indexing algorithms.\n By including both wrought and AM material states, this dataset offers\n valuable insight into the influence of manufacturing route on\n crystallographic texture and cellular dislocation structure in Inconel\n 718, a critical alloy for high-temperature structural applications."],"Methods":["Materials and\n Sample Preparation: Three different nickel-based\n superalloys were used in this study: a wrought recrystallized Inconel 718\n (30 minutes at 1050°C followed by 8 hours at 720°C) with chemical\n composition of (wt.%) Ni – 0.56% Al – 17.31% Fe – 0.14% Co – 17.97% Cr –\n 5.4% Nb + Ta – 1.00% Ti – 0.023% C – 0.0062% N; a 3D-printed Inconel 718\n by DED (as-built) and a dynamically recrystallized Waspalloy\n (heat-treated) characterized by a necklace microstructure. The 3D-printed\n material was produced using a Formalloy L2 Directed Energy Deposition\n (DED) unit utilizing a 650 W Nuburu 450 nm blue laser capable of achieving\n a 400 μm laser spot size. Argon was used as the shielding and carrier gas,\n and the specimen remained in its as-built condition. The chemical\n composition is in wt.%: Ni – 0.45% Al – 18.77% Fe – 0.07% Co – 18.88% Cr –\n 5.08% Nb – 0.96% Ti – 0.036% C – 0.02% Cu - 0.04% Mn - 0.08% Si - 3.04%\n Mo. All samples were machined by EDM as flat dogbone samples of gauge\n section 1 × 3 mm2. All samples were mechanically\n polished using abrasive papers, followed by diamond suspension down to 3\n μm, and were finished using a 50 nm colloidal silica suspension.\n Electron\n BackScatter Diffraction: EBSD measurements were\n performed on a Thermo Fisher Scios 2 Dual Beam FIB-SEM with an EDAX\n OIM-Hikari detector at an accelerating voltage of 20 kV, current of 6.4\n nA, an exposure time of 8.5 ms per diffraction pattern, 12 mm of working\n distance, and a 70° tilt. In total, 3 maps of 1000 × 900 μm were collected\n with a 1 μm step size, and 4 additional maps were collected at 0.1 μm step\n size. These EBSD maps were saved to .ang files and processed using the\n MTEX toolbox1. For each of these maps, SEM signal, confidence index (CI),\n and image quality (IQ) are provided as .tif files. The orientation maps\n are transformed using the inverse pole figure MTEX coloring [2] (given as\n IPF_mtex.jpg) and provided for the X (horizontal), Y (vertical), and Z\n (normal) directions. Additionally, all Kikuchi patterns were saved with no\n binning to 16-bit images under the .up2 format. Based on the diffraction\n patterns, sharpness maps, indicating the diffuseness of Kikuchi bands [3],\n have been constructed using EMSPHINX software [4] and are provided as .tif\n files. The details on the pattern center are provided in the .ang\n file. Kikuchi\n Patterns preprocessing: The Kikuchi patterns were\n originally acquired using 1 × 1 binning at a resolution of 480 × 480\n pixels. For the purpose of data processing, two versions are provided with\n the initial 1 × 1 binning and with a 4 × 4 binning (resulting in a reduced\n resolution of 120 × 120 pixels). Additional .up2 files, referred to as\n "preprocessed", are provided in which the background was\n subtracted and pattern gray values have been rescaled to fill the complete\n 16-bit range (between 0 and 65535). Due to the large size of the raw,\n unbinned data, they are not hosted on Dryad but can be made available upon\n request to the authors. Files\n Provided: The nomenclature of the provided files is\n described below, and a detailed explanation is available in the\n accompanying ReadMe.txt file, formatted according to DRYAD\n recommendations. The labels 718RX, AM718, and Waspalloy correspond to the\n wrought recrystallized Inconel 718, the as-built additively manufactured\n Inconel 718 (produced by DED), and a partially recrystallized Waspalloy,\n respectively. The term 1um refers to maps collected with a spatial\n resolution of 1 um, while 0.1um_1 and 0.1um_2 denote two separate maps\n acquired at 0.1 um resolution. The file labeled sharpness contains\n sharpness maps, as defined in [3], and computed using the EMSPHINX\n software [4]. Files labeled CI, IQ, and SEM represent the Confidence\n Index, Image Quality, and associated SEM maps obtained using MTEX1 and are\n provided as .tif files. Similarly, IPF_X, IPF_Y, and IPF_Z refer to\n inverse pole figure maps along the X (horizontal), Y (vertical), and Z\n (normal) directions and are provided as .jpg files. The file IPF_mtex\n gives the associated inverse pole figure MTEX coloring [1, 2]. 480x480 and\n 120x120 indicate the diffraction pattern resolutions, with the initial\n binning and with the 4 x 4 binning operation, respectively. All the images\n are stored as .up2 files. Files denoted as 120×120_preprocessed include\n the corresponding preprocessed patterns at the 120 x 120 resolution. The\n preprocessing procedure is detailed in the section "Kikuchi Patterns\n preprocessing." File\n format: The .up2 file is a proprietary data format\n used by EDAX/TSL systems to store Kikuchi pattern images and associated\n metadata from electron backscatter diffraction (EBSD) experiments. Each\n .up2 file contains high-resolution diffraction patterns acquired at each\n scan point, typically stored in a compressed or indexed form for efficient\n access. These files are commonly used when raw Kikuchi patterns are\n required for post-processing, including pattern remapping, machine\n learning applications, or simulation-based indexing. In addition to image\n data, .up2 files also include key acquisition parameters such as beam\n voltage, working distance, detector settings, image resolution, and stage\n coordinates, enabling full traceability of each pattern to its spatial\n location in the sample. The .ang file is a widely used text-based format\n for storing processed electron backscatter diffraction (EBSD) data.\n Generated by EDAX/TSL OIM software, it contains orientation mapping\n results after successful indexing of Kikuchi patterns. Each row in an .ang\n file corresponds to a single scan point and includes key information such\n as spatial coordinates (X, Y), Euler angles (Phi1, PHI, Phi2) defining\n crystallographic orientation, image quality (IQ), confidence index (CI),\n phase ID, and other optional metrics (e.g., grain ID or local\n misorientation). The file begins with a header that describes metadata,\n including step size, scan grid type (square or hexagonal), phase\n information, and scanning parameters. .ang files are commonly used for\n downstream analyses such as grain reconstruction, texture analysis, and\n misorientation mapping, and are often imported into visualization tools\n like MTEX toolbox1 or Dream.3D for further processing. The .tif (Tagged\n Image File Format) is a high-fidelity raster image format widely used in\n scientific imaging due to its ability to store uncompressed or losslessly\n compressed image data. In the context of EBSD datasets, .tif files\n typically store individual Kikuchi diffraction patterns collected during a\n scan. When used within a .up2 dataset, each pattern is saved as a separate\n .tif file, preserving the original grayscale intensity distribution\n necessary for accurate post-processing tasks such as reindexing, pattern\n matching, or machine learning-based classification. These images often\n have high bit-depth (e.g., 12-bit or 16-bit grayscale) to retain subtle\n contrast variations in the diffraction bands, which are critical for\n crystallographic orientation determination. The file naming and\n organization are indexed and referenced by the accompanying .up2 metadata\n file to maintain spatial correlation with the scan grid. The .jpg (or\n .jpeg), standing for Joint Photographic Experts Group, file format is a\n commonly used compressed image format designed to store photographic and\n continuous-tone images efficiently. .jpg uses lossy compression, meaning\n some image detail is discarded to significantly reduce file size. This\n makes it suitable for visual display and documentation purposes, but less\n ideal for quantitative image analysis, where preserving original pixel\n intensity values is critical. References:\n Bachmann, F., Hielscher, R. & Schaeben, H.\n Texture analysis with mtex–free and open source software toolbox.\n Solid state phenomena 160, 63–68 (2010).\n Nolze, G. & Hielscher, R. Orientations–perfectly\n colored. Appl. Crystallogr. 49, 1786–1802\n (2016). Wang, F. et al. Dislocation cells in\n additively manufactured metallic alloys characterized by electron\n backscatter diffraction pattern sharpness. Mater.\n Charact. 197, 112673 (2023). EMsoft-org.\n EMSphInx: Spherical indexing software for diffraction patterns. Public\n beta release; GPL-2.0 license. \n Acknowledgments: M.C., H.W., K.V., and J.C.S.\n are grateful for financial support from the Defense Advanced Research\n Projects Agency (DARPA - HR001124C0394). C.B., D.A., and J.C.S.\n acknowledge the NSF (award #2338346) for financial support. This work was\n carried out in the Materials Research Laboratory Central Research\n Facilities, University of Illinois. Carpenter Technology is acknowledged\n for providing the 718 and Waspalloy material. Tresa Pollock, McLean\n Echlin, and James Lamb are acknowledged for their support on the EBSD\n sharpness calculations."],"TechnicalInfo":["# Kikuchi pattern dataset from wrought and as-built additively\n manufactured superalloys Dataset DOI:\n [10.5061/dryad.zcrjdfnr9](10.5061/dryad.zcrjdfnr9) ## Description of the\n data and file structure #### Files Provided: See the Methods section for a\n description of file naming patterns and meaning. #### Folder architecture:\n 718RX: * 1um * 718RX_1um.ang * 718RX_1um_sharpness.tif * 718RX_1um_CI.tif\n * 718RX_1um_IQ.tif * 718RX_1um_SEM.tif * IPF_mtex.jpg *\n 718RX_1um_IPF_X.jpg * 718RX_1um_IPF_Y.jpg * 718RX_1um_IPF_Z.jpg *\n 718RX_1um_480x480.up2 * 718RX_1um_120x120.up2 *\n 718RX_1um_120x120_preprocessed.up2 * 0.1um_1 * 718RX_0.1um_1.ang *\n 718RX_0.1um_1_sharpness.tif * 718RX_0.1um_1_CI.tif * 718RX_0.1um_1_IQ.tif\n * 718RX_0.1um_1_SEM.tif * IPF_mtex.jpg * 718RX_0.1um_1_IPF_X.jpg *\n 718RX_0.1um_1_IPF_Y.jpg * 718RX_0.1um_1_IPF_Z.jpg *\n 718RX_0.1um_1_480x480.up2 * 718RX_0.1um_1_120x120.up2 *\n 718RX_0.1um_1_120x120_preprocessed.up2 * 0.1um_2 * 718RX_0.1um_2.ang *\n 718RX_0.1um_2_sharpness.tif * 718RX_0.1um_2_CI.tif * 718RX_0.1um_2_IQ.tif\n * 718RX_0.1um_2_SEM.tif * IPF_mtex.jpg * 718RX_0.1um_2_IPF_X.jpg *\n 718RX_0.1um_2_IPF_Y.jpg * 718RX_0.1um_2_IPF_Z.jpg *\n 718RX_0.1um_2_480x480.up2 * 718RX_0.1um_2_120x120.up2 *\n 718RX_0.1um_2_120x120_preprocessed.up2 AM718: * 1um * AM718_1um.ang *\n AM718_1um_sharpness.tif * AM718_1um_CI.tif * AM718_1um_IQ.tif *\n AM718_1um_SEM.tif * IPF_mtex.jpg * AM718_1um_IPF_X.jpg *\n AM718_1um_IPF_Y.jpg * AM718_1um_IPF_Z.jpg * AM718_1um_480x480.up2 *\n AM718_1um_120x120.up2 * AM718_1um_120x120_preprocessed.up2 * 0.1um_1 *\n AM718_0.1um_1.ang * AM718_0.1um_1_sharpness.tif * AM718_0.1um_1_CI.tif *\n AM718_0.1um_1_IQ.tif * AM718_0.1um_1_SEM.tif * IPF_mtex.jpg *\n AM718_0.1um_1_IPF_X.jpg * AM718_0.1um_1_IPF_Y.jpg *\n AM718_0.1um_1_IPF_Z.jpg * AM718_0.1um_1_480x480.up2 *\n AM718_0.1um_1_120x120.up2 * AM718_0.1um_1_120x120_preprocessed.up2 *\n 0.1um_2 * AM718_0.1um_2.ang * AM718_0.1um_2_sharpness.tif *\n AM718_0.1um_2_CI.tif * AM718_0.1um_2_IQ.tif * AM718_0.1um_2_SEM.tif *\n IPF_mtex.jpg * AM718_0.1um_2_IPF_X.jpg * AM718_0.1um_2_IPF_Y.jpg *\n AM718_0.1um_2_IPF_Z.jpg * AM718_0.1um_2_480x480.up2 *\n AM718_0.1um_2_120x120.up2 * AM718_0.1um_2_120x120_preprocessed.up2\n Waspalloy: * 1um * Waspalloy_1um.ang * Waspalloy_1um_sharpness.tif *\n Waspalloy_1um_CI.tif * Waspalloy_1um_IQ.tif * Waspalloy_1um_SEM.tif *\n IPF_mtex.jpg * Waspalloy_1um_IPF_X.jpg * Waspalloy_1um_IPF_Y.jpg *\n Waspalloy_1um_IPF_Z.jpg * Waspalloy_1um_480x480.up2 *\n Waspalloy_1um_120x120.up2 * Waspalloy_1um_120x120_preprocessed.up2 ##\n Code/software See Methods for recommendations on how to open files."]}more » « less
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Precession electron diffraction (PED) is a powerful technique for revealing the crystallographic orientation of samples at the nanoscale. However, the quality of orientation indexing is strongly influenced by the quality of diffraction patterns. In this study, we have developed a novel algorithm called Auto-CLAHE (automatic contrast-limited adaptive histogram equalization), which automatically enhances low-intensity diffraction pattern signals using contrast-limited adaptive histogram equalization (CLAHE). The degree of enhancement is dynamically adjusted based on the overall intensity of the diffraction pattern, with greater enhancement applied to patterns with fewer spots (i.e., away from zone axes) and little or no enhancement applied to patterns with many spots (i.e., at a zone axis). By improving the visibility of low-intensity diffraction spots, Auto-CLAHE significantly improves the template matching between experimentally acquired and simulated diffraction patterns, leading to orientation maps with dramatically higher quality and lower noise. We anticipate that Auto-CLAHE provides an efficient and practical solution for preprocessing PED data, enabling higher-quality crystal orientation mapping to be routinely obtained.more » « less
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