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oapen-20.500.12657-62301
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dspace
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oapen-20.500.12657-623012024-03-27T14:14:49Z Microstructure modeling and crystal plasticity parameter identification for predicting the cyclic mechanical behavior of polycrystalline metals Kuhn, Jannick Polykristalline Metalle; Ermüdung; Mikromechanische Modellierung; Laguerre-Kachelungen; Texturkoeffizienten-Optimierung; Polycrystalline metals; Fatigue; Micromechanical modeling; Laguerre tessellations; Texture coefficients optimization thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials Computational homogenization permits to capture the influence of the microstructure on the cyclic mechanical behavior of polycrystalline metals. In this work we investigate methods to compute Laguerre tessellations as computational cells of polycrystalline microstructures, propose a new method to assign crystallographic orientations to the Laguerre cells and use Bayesian optimization to find suitable parameters for the underlying micromechanical model from macroscopic experiments. 2023-04-07T10:04:47Z 2023-04-07T10:04:47Z 2023 book https://library.oapen.org/handle/20.500.12657/62301 eng Schriftenreihe Kontinuumsmechanik im Maschinenbau application/pdf Attribution-ShareAlike 4.0 International microstructure-modeling-and-crystal-plasticity-parameter-identification-for-predicting-the-cyclic-mechanical-behavior-of-polycrystalline-metals.pdf https://doi.org/10.5445/KSP/1000154640 KIT Scientific Publishing 10.5445/KSP/1000154640 10.5445/KSP/1000154640 44e29711-8d53-496b-85cc-3d10c9469be9 23 224 open access
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OAPEN
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DSpace
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English
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| description |
Computational homogenization permits to capture the influence of the microstructure on the cyclic mechanical behavior of polycrystalline metals. In this work we investigate methods to compute Laguerre tessellations as computational cells of polycrystalline microstructures, propose a new method to assign crystallographic orientations to the Laguerre cells and use Bayesian optimization to find suitable parameters for the underlying micromechanical model from macroscopic experiments.
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| title |
microstructure-modeling-and-crystal-plasticity-parameter-identification-for-predicting-the-cyclic-mechanical-behavior-of-polycrystalline-metals.pdf
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| spellingShingle |
microstructure-modeling-and-crystal-plasticity-parameter-identification-for-predicting-the-cyclic-mechanical-behavior-of-polycrystalline-metals.pdf
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| title_short |
microstructure-modeling-and-crystal-plasticity-parameter-identification-for-predicting-the-cyclic-mechanical-behavior-of-polycrystalline-metals.pdf
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| title_full |
microstructure-modeling-and-crystal-plasticity-parameter-identification-for-predicting-the-cyclic-mechanical-behavior-of-polycrystalline-metals.pdf
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| title_fullStr |
microstructure-modeling-and-crystal-plasticity-parameter-identification-for-predicting-the-cyclic-mechanical-behavior-of-polycrystalline-metals.pdf
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| title_full_unstemmed |
microstructure-modeling-and-crystal-plasticity-parameter-identification-for-predicting-the-cyclic-mechanical-behavior-of-polycrystalline-metals.pdf
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| title_sort |
microstructure-modeling-and-crystal-plasticity-parameter-identification-for-predicting-the-cyclic-mechanical-behavior-of-polycrystalline-metals.pdf
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| publisher |
KIT Scientific Publishing
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| publishDate |
2023
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| url |
https://doi.org/10.5445/KSP/1000154640
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| _version_ |
1799945220921491456
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