Micromechanical modeling approach to derive the yield surface for BCC and FCC steels using statistically informed microstructure models and nonlocal crystal plasticity

Vajragupta, Napat; Ahmed, Shahrior; Boeff, Martin; Ma, Anxin; Hartmaier, Alexander

doi:10.1134/s1029959917030109

Cited by 15 publications

(13 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another aspect to be discussed here is the use of micromechanical models to produce training data. With such models, the mechanical behavior of realistic microstructures can be simulated with crystal plasticity methods [7], providing an accurate description of the plastic properties of polycrystaline metals with different microstructures and crystallographic textures. One disadvantage of such micromechanical models is their tremendously high numerical effort making them unsuited for FEA applications of engineering structures that are much larger than the grain size of a material.…”

Section: Discussionmentioning

confidence: 99%

“…To determine these parameters, a series of experiments with different mutual orientations of loading axis and material axis is necessary. Alternatively, micromechanical models, in which discrete representations of the material's microstructure are used together with quite fundamental crystal plasticity models, can be used to calculate the anisotropic flow behavior of a polycrystalline metal [7,8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data-Oriented Constitutive Modeling of Plasticity in Metals

Hartmaier

2020

Materials

View full text Add to dashboard Cite

Constitutive models for plastic deformation of metals are typically based on flow rules determining the transition from elastic to plastic response of a material as function of the applied mechanical load. These flow rules are commonly formulated as a yield function, based on the equivalent stress and the yield strength of the material, and its derivatives. In this work, a novel mathematical formulation is developed that allows the efficient use of machine learning algorithms describing the elastic-plastic deformation of a solid under arbitrary mechanical loads and that can replace the standard yield functions with more flexible algorithms. By exploiting basic physical principles of elastic-plastic deformation, the dimensionality of the problem is reduced without loss of generality. The data-oriented approach inherently offers a great flexibility to handle different kinds of material anisotropy without the need for explicitly calculating a large number of model parameters. The applicability of this formulation in finite element analysis is demonstrated, and the results are compared to formulations based on Hill-like anisotropic plasticity as reference model. In future applications, the machine learning algorithm can be trained by hybrid experimental and numerical data, as for example obtained from fundamental micromechanical simulations based on crystal plasticity models. In this way, data-oriented constitutive modeling will also provide a new way to homogenize numerical results in a scale-bridging approach.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Oriented Constitutive Modeling of Plasticity in Metals

Hartmaier

2020

Materials

View full text Add to dashboard Cite

show abstract

“…Here, an example of this process is presented for Rolled-Cu. For details of the applied periodic boundary conditions and homogenization scheme, please refer to Vajragupta et al (2017). We choose a local crystal plasticity (CP) model without the effect of the strain gradient as described by Ma & Hartmaier (2014) for numerical modeling of material behavior.…”

Section: Applicationmentioning

confidence: 99%

“…12). For details of the applied periodic boundary conditions and homogenization scheme, please refer to Vajragupta et al (2017).…”

Section: Applicationmentioning

confidence: 99%

Optimized reconstruction of the crystallographic orientation density function based on a reduced set of orientations

et al. 2020

View full text Add to dashboard Cite

Crystallographic textures, as they develop for example during cold forming, can have a significant influence on the mechanical properties of metals, such as plastic anisotropy. Textures are typically characterized by a non-uniform distribution of crystallographic orientations that can be measured by diffraction experiments like electron backscatter diffraction (EBSD). Such experimental data usually contain a large number of data points, which must be significantly reduced to be used for numerical modeling. However, the challenge in such data reduction is to preserve the important characteristics of the experimental data, while reducing the volume and preserving the computational efficiency of the numerical model. For example, in micromechanical modeling, representative volume elements (RVEs) of the real microstructure are generated and the mechanical properties of these RVEs are studied by the crystal plasticity finite element method. In this work, a new method is developed for extracting a reduced set of orientations from EBSD data containing a large number of orientations. This approach is based on the established integer approximation method and it minimizes its shortcomings. Furthermore, the L 1 norm is applied as an error function; this is commonly used in texture analysis for quantitative assessment of the degree of approximation and can be used to control the convergence behavior. The method is tested on four experimental data sets to demonstrate its capabilities. This new method for the purposeful reduction of a set of orientations into equally weighted orientations is not only suitable for numerical simulation but also shows improvement in results in comparison with other available methods.

show abstract

“…However, describing the detailed deformation mechanism occurring in a material is inherently difficult [7][8][9]. To understand the deformation mechanism at the grain scale, the micromechanical modeling approach is applied [10]. The micromechanical model uses microstructural features in combination with a material model to reflect the behavior of crystalline materials.…”

Section: Introductionmentioning

confidence: 99%

Robust Optimization Scheme for Inverse Method for Crystal Plasticity Model Parametrization

2020

View full text Add to dashboard Cite

A bottom-up material modeling based on a nonlocal crystal plasticity model requires information of a large set of physical and phenomenological parameters. Because of the many material parameters, it is inherently difficult to determine the nonlocal crystal plasticity parameters. Therefore, a robust method is proposed to parameterize the nonlocal crystal plasticity model of a body-centered cubic (BCC) material by combining a nanoindentation test and inverse analysis. Nanoindentation tests returned the load–displacement curve and surface imprint of the considered sample. The inverse analysis is developed based on trust-region-reflective algorithm, which is the most robust optimization algorithm for the considered non-convex problem. The discrepancy function is defined to minimize both the load–displacement curves and the surface topologies of the considered material under applying varied indentation forces obtained from numerical models and experimental output. The numerical model results based on the identified material properties show good agreement with the experimental output. Finally, a sensitivity analysis performed changing the nonlocal crystal plasticity parameters in a predefined range emphasized that the geometrical factor has the most significant influence on the load–displacement curve and surface imprint parameters.

show abstract

Micromechanical modeling approach to derive the yield surface for BCC and FCC steels using statistically informed microstructure models and nonlocal crystal plasticity

Cited by 15 publications

References 30 publications

Data-Oriented Constitutive Modeling of Plasticity in Metals

Data-Oriented Constitutive Modeling of Plasticity in Metals

Optimized reconstruction of the crystallographic orientation density function based on a reduced set of orientations

Robust Optimization Scheme for Inverse Method for Crystal Plasticity Model Parametrization

Contact Info

Product

Resources

About