| 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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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
Places of action
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article
Data-oriented description of texture-dependent anisotropic material behavior
Abstract
In metallurgical processes, as for example cold rolling or deep drawing of sheet metal, it is frequently observed that the crystallographic texture, and with it the anisotropic mechanical properties of a material, evolve dynamically. Hence, to describe such processes, it is necessary to model the functional dependence of anisotropic material parameters on the texture, which itself can vary locally with the different plastic strain histories. In this work, we present a new data-oriented approach to parametrize the anisotropic yield function Barlat Yld2004-18p from micromechanical simulations for different textures. This is accomplished by applying supervised machine learning (ML) methods to express the relationship between different crystallographic textures and the material parameters of the yield function. The crystallographic textures are chosen to vary continuously between a random texture on the one hand side, and a unimodal Goss or Copper texture the other. These crystallographic textures are rather common in sheet metal forming. In this way, furthermore, the transition from isotropic plasticity to a rather severe case of anisotropy can be modeled, which is thought to mimic the dynamical evolution of the texture in a metallurgical process. It is found that a regularization strategy is necessary to circumvent the known non-uniqueness between Yld2004-18p parameters and the resulting plastic yield behavior. After this regularization, a unique relationship between the material parameters and the yield onset is established, making it possible to train different ML models with excellent accuracy and generalization properties to anisotropic plastic material behavior. The trained ML models are able to reliably predict the coefficients of unknown textures even with a small amount of training data and, thus, to correctly represent the yield behavior resulting from the various textures. The proposed method represents an efficient extension of the description of anisotropic plastic yielding as it establishes a ...