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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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Biswas, Abhishek
VTT Technical Research Centre of Finland
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (27/27 displayed)
- 2024On the grain level deformation of BCC metals with crystal plasticity modeling:Application to an RPV steel and the effect of irradiationcitations
- 2024Analysis of rolling contact and tooth root bending fatigue in a new high-strength steel:Experiments and micromechanical modellingcitations
- 2024On the grain level deformation of BCC metals with crystal plasticity modelingcitations
- 2024Crystal plasticity model for creep and relaxation deformation of OFP coppercitations
- 2024Analysis of rolling contact and tooth root bending fatigue in a new high-strength steel: Experiments and micromechanical modellingcitations
- 2023Estimating Long Term Behaviour Of DED-printed AlCoNiFe Alloy
- 2023Estimating Long Term Behaviour Of DED-printed AlCoNiFe Alloy
- 2023Micromechanical modeling of single crystal and polycrystalline UO2 at elevated temperaturescitations
- 2023Performance Driven Design And Modeling Of Compositionally Complex AM Al-Co-Ni-Fe Alloys
- 2023Performance Driven Design And Modeling Of Compositionally Complex AM Al-Co-Ni-Fe Alloys
- 2023Crystal plasticity model for creep and relaxation deformation of OFP coppercitations
- 2023Experimental Assessment and Micromechanical Modeling of Additively Manufactured Austenitic Steels under Cyclic Loadingcitations
- 2023Micromechanical modeling of single crystal and polycrystalline UO 2 at elevated temperaturescitations
- 2023Predicting anisotropic behavior of textured PBF-LB materials via microstructural modelingcitations
- 2022Micromechanical Modeling of AlSi10Mg Processed by Laser-Based Additive Manufacturing: From as-Built to Heat-Treated Microstructurescitations
- 2022Micromechanical Modeling of AlSi10Mg Processed by Laser-Based Additive Manufacturing: From as-Built to Heat-Treated Microstructures
- 2022A hybrid approach for the efficient computation of polycrystalline yield loci with the accuracy of the crystal plasticity finite element method
- 2022Data-oriented description of texture-dependent anisotropic material behaviorcitations
- 2022Identification of texture characteristics for improved creep behavior of a L-PBF fabricated IN738 alloy through micromechanical simulationscitations
- 2022Micromechanical Modeling of AlSi10Mg Processed by Laser-Based Additive Manufacturing:From as-Built to Heat-Treated Microstructurescitations
- 2020Influence of Pore Characteristics on Anisotropic Mechanical Behavior of Laser Powder Bed Fusion–Manufactured Metal by Micromechanical Modelingcitations
- 2020Study of the influence of microstructural features of 316L stainless steal produced by selective laser melting on its mechanical properties
- 2020Optimized reconstruction of the crystallographic orientation density function based on a reduced set of orientationscitations
- 2020Optimized reconstruction of the crystallographic orientation density function based on a reduced set of orientationscitations
- 2020Effect of grain statistics on micromechanical modeling
- 2020Influence of pore characteristics on anisotropic mechanical behavior of laser powder bed fusion–manufactured metal by micromechanical modelingcitations
- 2019Optimized reconstruction of the crystallographic orientation density function based on a reduced set of orientations
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article
Optimized reconstruction of the crystallographic orientation density function based on a reduced set of orientations
Abstract
<jats:p>Crystallographic textures, as they develop for example during cold forming, can have a significant influence on the mechanical properties of metals, such as plastic anisotropy. Textures are typically characterized by a non-uniform distribution of crystallographic orientations that can be measured by diffraction experiments like electron backscatter diffraction (EBSD). Such experimental data usually contain a large number of data points, which must be significantly reduced to be used for numerical modeling. However, the challenge in such data reduction is to preserve the important characteristics of the experimental data, while reducing the volume and preserving the computational efficiency of the numerical model. For example, in micromechanical modeling, representative volume elements (RVEs) of the real microstructure are generated and the mechanical properties of these RVEs are studied by the crystal plasticity finite element method. In this work, a new method is developed for extracting a reduced set of orientations from EBSD data containing a large number of orientations. This approach is based on the established integer approximation method and it minimizes its shortcomings. Furthermore, the<jats:italic>L</jats:italic><jats:sup>1</jats:sup>norm is applied as an error function; this is commonly used in texture analysis for quantitative assessment of the degree of approximation and can be used to control the convergence behavior. The method is tested on four experimental data sets to demonstrate its capabilities. This new method for the purposeful reduction of a set of orientations into equally weighted orientations is not only suitable for numerical simulation but also shows improvement in results in comparison with other available methods.</jats:p>