| People | Locations | Statistics |
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| Naji, M. |
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| Motta, Antonella |
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| Aletan, Dirar |
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| Mohamed, Tarek |
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| Ertürk, Emre |
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| Taccardi, Nicola |
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| Kononenko, Denys |
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| Petrov, R. H. | Madrid |
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| Alshaaer, Mazen | Brussels |
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| Bih, L. |
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| Casati, R. |
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| Muller, Hermance |
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| Kočí, Jan | Prague |
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| Šuljagić, Marija |
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| Kalteremidou, Kalliopi-Artemi | Brussels |
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| Azam, Siraj |
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| Ospanova, Alyiya |
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| Blanpain, Bart |
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| Ali, M. A. |
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| Popa, V. |
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| Rančić, M. |
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| Ollier, Nadège |
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| Azevedo, Nuno Monteiro |
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| Landes, Michael |
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| Rignanese, Gian-Marco |
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Poulsen, Henning, F.
Technical University of Denmark
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (28/28 displayed)
- 20243D microstructural and strain evolution during the early stages of tensile deformationcitations
- 2024Microstructure and stress mapping in 3D at industrially relevant degrees of plastic deformationcitations
- 2023Exploring 4D microstructural evolution in a heavily deformed ferritic alloycitations
- 2023Inferring the probability distribution over strain tensors in polycrystals from diffraction based measurementscitations
- 2022High-resolution 3D X-ray diffraction microscopy: 3D mapping of deformed metal microstructurescitations
- 2022Multiscale Exploration of Texture and Microstructure Development in Recrystallization Annealing of Heavily Deformed Ferritic Alloyscitations
- 2022Multiscale characterisation of strains in semicrystalline polymers
- 20224D microstructural evolution in a heavily deformed ferritic alloycitations
- 2020Grain boundary mobilities in polycrystalscitations
- 2019Fast and quantitative 2D and 3D orientation mapping using Raman microscopycitations
- 2018Three-dimensional grain growth in pure iron. Part I. statistics on the grain levelcitations
- 2017Determining material parameters using phase-field simulations and experimentscitations
- 2017Ultra-low-angle boundary networks within recrystallizing grainscitations
- 2015Injection molded polymeric hard X-ray lensescitations
- 2014High-Resolution Reciprocal Space Mapping for Characterizing Deformation Structurescitations
- 2012X-ray diffraction contrast tomography (DCT) system, and an X-ray diffraction contrast tomography (DCT) method
- 2011On the Use of Laguerre Tessellations for Representations of 3D Grain Structurescitations
- 2011Grain-resolved elastic strains in deformed copper measured by three-dimensional X-ray diffractioncitations
- 2011Three-Dimensional Orientation Mapping in the Transmission Electron Microscopecitations
- 2009Structured scintillators for X-ray imaging with micrometre resolutioncitations
- 2009New opportunities for 3D materials science of polycrystalline materials at the micrometre lengthscale by combined use of X-ray diffraction and X-ray imagingcitations
- 2009Measuring the elastic strain of individual grains in polycrystalline materials
- 2008A high-spatial-resolution three-dimensional detector array for 30-200 keV X-rays based on structured scintillatorscitations
- 2004Simultaneous measurement of the strain tensor of 10 individual grains embedded in an Al tensile samplecitations
- 2004Measurement of the components of plastic displacement gradients in three dimensionscitations
- 2004Metal Microstructures in Four Dimensions
- 20023DXRD microscopy - a comparison with neutron diffractioncitations
- 2000A high energy microscope for local strain measurements within bulk materials
Places of action
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article
Inferring the probability distribution over strain tensors in polycrystals from diffraction based measurements
Abstract
Polycrystals illuminated by high-energy X-rays or neutrons produce diffraction patterns in which the measured diffraction peaks encode the individual single crystal strain states. While state of the art X-ray and neutron diffraction approaches can be used to routinely recover per grain mean strain tensors, less work has been produced on the recovery of higher order statistics of the strain distributions across the individual grains. In the setting of small deformations, we consider the problem of estimating the crystal elastic strain tensor probability distribution from diffraction data. For the special case of multivariate Gaussian strain tensor probability distributions, we show that while the mean of the distribution is well defined from measurements, the covariance of strain has a null-space. We show that there exist exactly 6 orthogonal perturbations to this covariance matrix under which the measured strain signal is invariant. In particular, we provide analytical parametrisations of these perturbations together with the set of possible maximum-likelihood estimates for a multivariate Gaussian fit to data. The parametric description of the null-space provides insights into the strain PDF modes that cannot be accurately estimated from the diffraction data. Understanding these modes prevents erroneous conclusions from being drawn based on the data. Beyond Gaussian strain tensor probability densities, we derive an iterative radial basis regression scheme in which the strain tensor probability density is estimated by a sparse finite basis expansion. This is made possible by showing that the operator mapping the strain tensor probability density onto the measured histograms of directional strain is linear, without approximation. The utility of the proposed algorithm is demonstrated by numerical simulations in the setting of single crystal monochromatic X-ray scattering. The proposed regression methods were found to robustly reject outliers and accurately predict the strain tensor probability distributions in the ...