| 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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Vajragupta, Napat
VTT Technical Research Centre of Finland
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (21/21 displayed)
- 2023Micromechanical modeling of single crystal and polycrystalline UO2 at elevated temperaturescitations
- 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
- 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
- 2021Finite element modeling of brittle and ductile modes in cutting of 3C-SiC
- 2021Influence of crystal plasticity parameters on the strain hardening behavior of polycrystalscitations
- 2020Influence of Pore Characteristics on Anisotropic Mechanical Behavior of Laser Powder Bed Fusion–Manufactured Metal by Micromechanical Modelingcitations
- 2020A comparative study of an isotropic and anistropic model to describe themicro-indentation of TWIP steel
- 2020Influence of trapped gas on pore healing under hot isostatic pressing in nickel-base superalloys
- 2020Micromechanical modeling of DP600 steelcitations
- 2020Optimized reconstruction of the crystallographic orientation density function based on a reduced set of orientationscitations
- 2020Robust optimization scheme for inverse method for crystal plasticity model parametrizationcitations
- 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
- 2019Studying Grain Boundary Strengthening by Dislocation-Based Strain Gradient Crystal Plasticity Coupled with a Multi-Phase-Field Modelcitations
- 2019Modeling macroscopic material behavior with machine learning algorithms trained by micromechanical simulations
- 2019Studying grain boundary strengthening by dislocation-based strain gradient crystal plasticity coupled with a multi-phase-field model
- 2019Parameterization of a non-local crystal plasticity model for tempered lath martensite using nanoindentation and inverse method
- 2019Optimized reconstruction of the crystallographic orientation density function based on a reduced set of orientations
- 2014Modeling the microstructure influence on fatigue life variability in structural steels
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 ...