Micromechanical modeling of fatigue crack initiation in polycrystals

Boeff, Martin; Hassan, Hamad ul; Hartmaier, Alexander

doi:10.1557/jmr.2017.384

Cited by 38 publications

(28 citation statements)

References 41 publications

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“…The described model consists of a geometrical description of the grain structure of a polycrystal with equiaxed grains. This microstructure model is generated with a so-called dynamic microstructure generator (DMG) (Boeff, 2016) based on particle simulation to distribute the centers for a subsequent radical Voronoi tessellation. The constitutive modeling of plastic deformation in the individual grains is carried out with a crystal plasticity method implemented as user-defined material model (UMAT) for ABAQUS.…”

Section: Materials Modelingmentioning

confidence: 99%

“…For the investigation in both applications, quasi-2D representative volume elements (RVEs) were generated using the DMG, which couples a particle simulation method with a radical Voronoi tessellation algorithm (Boeff, 2016). In the first step, the target grain size distribution is determined via a log-normal distribution.…”

Section: Representative Volume Elementmentioning

confidence: 99%

“…Therefore, macroscopic quantities can be homogenized directly from nodal displacement, reaction force, and position vector of these reference nodes as introduced in Kulosa et al (2017). For further details on the implemented homogenization technique, the reader is kindly referred to Boeff (2016), Kulosa et al (2017). The macroscopic strain tensor can be formulated from the nodal displacement u node i and be mathematically expressed as:…”

Section: Homogenization Of Macroscopic Stress and Strain Tensors Frommentioning

confidence: 99%

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Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations

et al. 2019

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Micromechanical modeling of material behavior has become an accepted approach to describe the macroscopic mechanical properties of polycrystalline materials in a microstructure-sensitive way. The microstructure is modeled by a representative volume element (RVE), and the anisotropic mechanical behavior of individual grains is described by a crystal plasticity model. Such micromechanical models are subjected to mechanical loads in a finite element (FE) simulation and their macroscopic behavior is obtained from a homogenization procedure. However, such micromechanical simulations with a discrete representation of the material microstructure are computationally very expensive, in particular when conducted for 3D models, such that it is prohibitive to apply them for process simulations of macroscopic components. In this work, we suggest a new approach to develop microstructure-sensitive, yet flexible and numerically efficient macroscopic material models by using micromechanical simulations for training Machine Learning (ML) algorithms to capture the mechanical response of various microstructures under different loads. In this way, the trained ML algorithms represent a new macroscopic constitutive relation, which is demonstrated here for the case of damage modeling. In a second application of the combination of ML algorithms and micromechanical modeling, a proof of concept is presented for the application of trained ML algorithms for microstructure design with respect to desired mechanical properties. The input data consist of different stress-strain curves obtained from micromechanical simulations of uniaxial testing of a wide range of microstructures. The trained ML algorithm is then used to suggest grain size distributions, grain morphologies and crystallographic textures, which yield the desired mechanical response for a given application. For validation purposes, the resulting grain microstructure parameters are used to generate RVEs, accordingly and the macroscopic stress-strain curves for those microstructures are calculated and compared with the target quantities. The two examples presented in this work, demonstrate clearly that ML methods can be trained by micromechanical simulations, which capture material behavior and its relation to Reimann et al. Application of Micromechanical Modeling on Machine Learningmicrostructural mechanisms in a physically sound way. Since the quality of the ML algorithms is only as good as that of the micromechanical model, it is essential to validate these models properly. Furthermore, this approach allows a hybridization of experimental and numerical data.

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Section: Materials Modelingmentioning

confidence: 99%

Section: Representative Volume Elementmentioning

confidence: 99%

Section: Homogenization Of Macroscopic Stress and Strain Tensors Frommentioning

confidence: 99%

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Modeling Macroscopic Material Behavior With Machine Learning Algorithms Trained by Micromechanical Simulations

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“…In this chapter two applications of the multiphase field method are presented, that are more of theoretical interest and not directly related to materials. The first is the construction of generator points for a Laguerre tessellation that recover a certain grain size distribution given by a histogram, which is an interesting mathematical problem [74,75,76]. The second application is the utilization of the multiphase field method itself as a load-balancer.…”

Section: Transfer Of the Multiphase Field Methodsmentioning

confidence: 99%

“…In the following a way of generating representative volume elements (RVE) for polycrystalline materials with a given grain size distribution is presented. The generated RVEs are useful for FEM simulations of the mechanical behavior of polycrystalline materials [75,76].…”

Section: Methodsmentioning

confidence: 99%

Massively Parallel Multiphase Field Simulations

Tegeler¹,

Sutmann

Monas³

Proceedings of the Fourth International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering

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The multiphase field method is a commonly used tool to simulate the evolution of microstructure during materials processing. Even when using a concept called active parameter tracking, which substantially reduces the computational complexity, the resource demands of the multiphase field method remain significant in both time and memory. A hybrid-parallelization is presented, that combines MPI and OpenMP and allows considerably larger system sizes. Due to the utilization of active parameter tracking the computational load can be heterogeneously distributed in the system, which makes load-balancing necessary in order to obtain an efficient parallelization. In this thesis two different load-balancing schemes are presented. The first uses graphpartitioning and an adaptive sub-domain decomposition. The second is based on the multiphase field method itself. Results are presented for performance benchmarks as well as for a variety of applications, including grain growth in polycrystalline materials with hundreds of thousands of different phase fields and Mg-Al alloy solidification as well as a coupling with the Lattice-Boltzmann method. One of the load-balancing schemes is also applied to a cell-based particle method. In addition a way of utilizing the multiphase field method is shown that allows the construction of microstructures and the corresponding Laguerre generator points with a given grain size distribution. 4Declaration/Erklärung I, Marvin Tegeler, born on July, 31st, 1985 in Gelsenkirchen, hereby declare that this dissertation is my own work.

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