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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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Jongbloed, Geurt
Delft University of Technology
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (6/6 displayed)
- 2022A Data-Driven Approach for Studying the Influence of Carbides on Work Hardening of Steelcitations
- 2021Microstructure–property relation and machine learning prediction of hole expansion capacity of high-strength steelscitations
- 2021Isotonic regression for metallic microstructure datacitations
- 2020General framework for testing Poisson-Voronoi assumption for real microstructurescitations
- 2020The combined influence of grain size distribution and dislocation density on hardness of interstitial free steelcitations
- 2019Accurate representation of the distributions of the 3D Poisson-Voronoi typical cell geometrical featurescitations
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article
Accurate representation of the distributions of the 3D Poisson-Voronoi typical cell geometrical features
Abstract
<p>Understanding the intricate and complex materials microstructure and how it is related to materials properties is an important problem in the Materials Science field. For a full comprehension of this relation, it is fundamental to be able to describe the main characteristics of the 3-dimensional microstructure. The most basic model used for approximating steel microstructure is the Poisson-Voronoi diagram. Poisson-Voronoi diagrams have interesting mathematical properties, and they are used as a good model for single-phase materials. In this paper we exploit the scaling property of the underlying Poisson process to derive the distribution of the main geometrical features of the grains for every value of the intensity parameter. Moreover, we use a sophisticated simulation program to construct a close Monte Carlo based approximation for the distributions of interest. Using this, we determine the closest approximating distributions within the mentioned frequently used parametric classes of distributions and conclude that these representations can be quite accurate. Finally we consider a 3D volume dataset and compare the real volume distribution to what is to be expected under the Poisson-Voronoi model.</p>