Topic
Electron backscatter diffraction
About: Electron backscatter diffraction is a research topic. Over the lifetime, 15184 publications have been published within this topic receiving 317847 citations. The topic is also known as: EBSD.
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TL;DR: In this paper, the degradation of polycrystalline superalloy 720Li is studied in air between room temperature and 1000°C. The physical factors responsible for the ductility dip are established using energy-dispersive X-ray spectroscopy, nanoscale secondary ion mass spectrometry and the analysis of electron backscatter diffraction patterns.
98 citations
TL;DR: In this paper, electron backscatter diffraction (EBSD) is applied to measure the plastic strain present in RR1000 nickel-based superalloy microstructure following thermo-mechanical fatigue tests.
98 citations
TL;DR: In this paper, a 304L austenitic stainless steel was subjected to different heat inputs by shielded metal arc welding process using a standard 308L electrode and microstructural developments were characterized by using optical microscopy and electron backscattered diffraction, while the residual stresses were measured by X-ray diffraction using the sin 2 ψ method.
98 citations
TL;DR: A machine learning model using a convolutional neural network is developed that automatically determines the crystal structure quickly and with high accuracy using EBSD patterns, providing a method for eliminating some of the guesswork from crystal structure determination.
Abstract: Electron backscatter diffraction (EBSD) is one of the primary tools for crystal structure determination. However, this method requires human input to select potential phases for Hough-based or dictionary pattern matching and is not well suited for phase identification. Automated phase identification is the first step in making EBSD into a high-throughput technique. We used a machine learning–based approach and developed a general methodology for rapid and autonomous identification of the crystal symmetry from EBSD patterns. We evaluated our algorithm with diffraction patterns from materials outside the training set. The neural network assigned importance to the same symmetry features that a crystallographer would use for structure identification.
98 citations
TL;DR: Vaterite, a polymorph of CaCO(3) was first mentioned by H. Vater in 1897, plays key roles in weathering and biomineralization processes, but occurs only in the form of nanosized crystals, unsuitable for structure determination.
Abstract: tion that is fundamental for understanding material properties. Still, a number of compounds have eluded such kinds of analysis because they are nanocrystalline, highly disordered, with strong pseudosymmetries or available only in small amounts in polyphasic or polymorphic systems. These materials are crystallographically intractable with conventional Xray or synchrotron radiation diffraction techniques. Single nanoparticles can be visualized by high-resolution transmission electron microscopy (HR-TEM) up to sub�ngstrom resolution, [2] but obtaining 3D information is still a difficult task, especially for highly beam-sensitive materials and crystal structures with long cell parameters. Electron diffraction (ED) delivers higher resolved data with a significant lower electron dose on the sample, but is biased by a substantial number of missing reflections and the occurrence of dynamic scattering that affects reflection intensities. [3] Therefore, ED is mainly used in combination with Xray powder diffraction and high-resolution electron microscopy. [4]
98 citations