| People | Locations | Statistics |
|---|---|---|
| Naji, M. |
| |
| Motta, Antonella |
| |
| Aletan, Dirar |
| |
| Mohamed, Tarek |
| |
| Ertürk, Emre |
| |
| Taccardi, Nicola |
| |
| Kononenko, Denys |
| |
| Petrov, R. H. | Madrid |
|
| Alshaaer, Mazen | Brussels |
|
| Bih, L. |
| |
| Casati, R. |
| |
| Muller, Hermance |
| |
| Kočí, Jan | Prague |
|
| Šuljagić, Marija |
| |
| Kalteremidou, Kalliopi-Artemi | Brussels |
|
| Azam, Siraj |
| |
| Ospanova, Alyiya |
| |
| Blanpain, Bart |
| |
| Ali, M. A. |
| |
| Popa, V. |
| |
| Rančić, M. |
| |
| Ollier, Nadège |
| |
| Azevedo, Nuno Monteiro |
| |
| Landes, Michael |
| |
| Rignanese, Gian-Marco |
|
Hartmaier, Alexander
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (54/54 displayed)
- 2024Micro-macro modeling of tensile behavior of a friction stir welded hybrid joint of AlSi10Mg parts produced by powder bed fusion and castingcitations
- 2023Three-dimensional microstructure reconstruction for two-phase materials from three orthogonal surface maps
- 2023Micromechanical modeling of the low-cycle fatigue behavior of additively manufactured AlSi10Mgcitations
- 2023Experimental Assessment and Micromechanical Modeling of Additively Manufactured Austenitic Steels under Cyclic Loadingcitations
- 2022Micromechanical Modeling of AlSi10Mg Processed by Laser-Based Additive Manufacturing: From as-Built to Heat-Treated Microstructurescitations
- 2022A hybrid approach for the efficient computation of polycrystalline yield loci with the accuracy of the crystal plasticity finite element method
- 2022Optimal data-generation strategy for machine learning yield functions in anisotropic plasticitycitations
- 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
- 2021Micro-, macromechanical and aeroelastic investigation of glass - fiber based, lightweight turbomachinery components
- 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
- 2020Influence of trapped gas on pore healing under hot isostatic pressing in nickel-base superalloys
- 2020Micromechanical modeling of DP600 steelcitations
- 2020The brittle-to-ductile transition in cold-rolled tungsten sheets: the rate-limiting mechanism of plasticity controlling the BDT in ultrafine-grained tungstencitations
- 2020Elucidating the dual role of grain boundaries as dislocation sources and obstacles and its impact on toughness and brittle-to-ductile transition
- 2020Data-oriented constitutive modeling of plasticity in metals
- 2020Optimized reconstruction of the crystallographic orientation density function based on a reduced set of orientationscitations
- 2020Hydrogen embrittlement at cleavage planes and grain boundaries in bcc iron-revisiting the first-principles cohesive zone model
- 2020Inverse method to determine fatigue properties of materials by combining cyclic indentation and numerical simulation
- 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
- 2019Influence of excess volumes induced by Re and W on dislocation motion and creep in Ni-base single crystal superalloys
- 2019Studying Grain Boundary Strengthening by Dislocation-Based Strain Gradient Crystal Plasticity Coupled with a Multi-Phase-Field Modelcitations
- 2019Micromechanical modelling of the influence of strain ratio on fatigue crack initiation in a martensitic steelcitations
- 2019Ab Initio Study of the Combined Effects of Alloying Elements and H on Grain Boundary Cohesion in Ferritic Steelscitations
- 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
- 2019Micromechanical modelling of the cyclic deformation behavior of martensitic SAE 4150
- 2019Modelling cyclic behaviour of martensitic steel with J2 plasticity and crystal plasticity
- 2019Ab initio study of the combined effects of alloying elements and H on grain boundary cohesion in ferritic steels
- 2019Optimized reconstruction of the crystallographic orientation density function based on a reduced set of orientations
- 2017A Study on Microstructural Parameters for the Characterization of Granular Porous Ceramics Using a Combination of Stochastic and Mechanical Modelingcitations
- 2017Micromechanical modeling of fatigue crack initiation in polycrystals
- 2016Microstructure design of tempered martensite by atomistically informed full-field simulation
- 2015Primary combination of phase-field and discrete dislocation dynamics methods for investigating athermal plastic deformation in various realistic Ni-base single crystal superalloy microstructurescitations
- 2015Primary combination of phase-field and discrete dislocation dynamics methods for investigating athermal plastic deformation in various realistic Ni-base single crystal superalloy microstructurescitations
- 2015Large scale Molecular Dynamics simulation of microstructure formation during thermal spraying of pure coppercitations
- 2014Modeling the microstructure influence on fatigue life variability in structural steels
- 2014Plastic deformation modelling of tempered martensite steel block structure by a nonlocal crystal plasticity model
- 2014Microstructural characterization of shape memory alloys on the atomic scale
- 2014Hydrogen embrittlement of a carbon segregated Sigma 5(310)[001] symmetrical tilt grain boundary in alpha-Fe
- 2013On the crystallographic anisotropy of nanoindentation in pseudoelastic NiTicitations
- 2012Mechanisms of crazing in glassy polymers revealed by molecular dynamics simulations
- 2012Atomistically informed crystal plasticity model for body-centered cubic iron
- 2011Mechanisms of grain boundary softening and strain-rate sensitivity in deformation of ultrafine-grained metals at high temperatures
- 2010Phase-field model with plastic flow for grain-growth in nanocrystalline materialcitations
- 2010How dislocation substructures evolve during long-term creep of a 12% Cr tempered martensitic ferritic steel
- 2009Pair vs many-body potentials: influence on elastic and plastic behavior in nanoindentation of fcc metals
- 2008The strength limit in a bio-inspired metallic nanocomposite
Places of action
| Organizations | Location | People |
|---|
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 ...