Virtual material testing for stamping simulations based on polycrystal plasticity

Kraska, Martin; Doig, M.; Tikhomirov, Dmitrij; Raabe, Dierk; Roters, Franz

doi:10.1016/j.commatsci.2009.03.025

Cited by 75 publications

(36 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These problems usually require appropriate homogenization schemes within a CPFE model since a larger number of crystals and/or phases must be considered in each representative volume element mapped at a FE integration point. Primary engineering objectives of CPFE applications in macroscopic forming simulations are the prediction of the precise material shape after forming, thickness distribution, material failure, optimization of material flow, elastic spring-back, forming limits, texture evolution and the mechanical properties of the formed part [13,[137][138][139]145,217]. Further related applications occur in tool design, press layout and surface properties (see references in Table 1).…”

Section: The Crystal Plasticity Finite-element Methods As a Multimechamentioning

confidence: 99%

Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: Theory, experiments, applications

et al. 2010

Self Cite

View full text Add to dashboard Cite

Section: The Crystal Plasticity Finite-element Methods As a Multimechamentioning

confidence: 99%

Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: Theory, experiments, applications

et al. 2010

Self Cite

View full text Add to dashboard Cite

“…[33][34][35] All these efforts were aimed at providing reliable material data while minimizing the experimental effort required for calibrating a yield function. [36][37][38] However, most models cannot predict yield loci with satisfactory accuracy. With this in mind, the CTFP model has been proposed based on the Taylor theories with manipulation as described briefly below.…”

Section: B Virtual Experiments and The Ctfp Modelmentioning

confidence: 99%

Failure Orientation in Stretch Forming and Its Correlation with a Polycrystal Plasticity-Based Material Model for a Collection of Highly Formable Sheet Steels

Boterman

Atzema

et al. 2016

Metall Mater Trans A

View full text Add to dashboard Cite

Robust design optimization techniques have been developed in recent years within the automotive industry with the aim of reducing scrap rates and improving process stability in sheet metal forming. These new techniques are able to take process variations and other sources of material scatter into account. Among the many material variables and inputs used, the yield criterion is an important aspect and this is used to describe the plastic behavior of sheet metals. To achieve a reliable output in an optimization study, the yield criterion selected must be representative of material response and scatter. However, simple material models that deviate from real material behavior are often used due to a lack of material data, which is usually a requirement when using more complex models. In the present research, a polycrystal plasticity-based CTFP model has been evaluated in stretch forming for a collection of highly formable sheet steel materials. The results demonstrate that the CTFP model can capture the yielding character and also detect the minor deviations presented by different coils. The stretching factor derived from the CTFP model, as opposed to the work hardening and ductility, has a dominant effect on failure for a collection of materials with similar mechanical properties.Results also indicate that plastic deformation causes texture evolution and, consequently, an evolving yield locus. Such changes in the yield locus during deformation have an effect on stretching and friction calibration in FE simulations.

show abstract

“…The boundary condition prescription was chosen to be same as that of Ref. [11]. Over the observed surface, all edge displacements (i.e., in the present 2D case, the displacements at the two end points) coincide with the "experimental" data (shown in green), as a result of the prescription of 4 in-situ SEM experiments.…”

Section: Choice Of Metric For Umentioning

confidence: 99%

“…The "experimental" macroscopic load F exp is estimated independently by integration on the edge subjected to tension from a numerical homogenization on a Representative Volume Element (RVE) [3,11,4], as shown in Figure 3, with the reference parameters of DD CC. The standard deviation of image noise, η f , is set to 2% of the gray level range of the "experimental" image of the surface and η F , the standard deviation of macroscopic load measurement is set to 2 N. The same RVE is used to calculate simulated macroscopic loads {F sim } during the identification procedure with the current estimate of material parameters {p m }.…”

Section: Proof Of Concept 31 Virtual Experimentsmentioning

confidence: 99%

Backtracking Depth-Resolved Microstructures for Crystal Plasticity Identification—Part 2: Identification

Shi¹,

Latourte²,

Hild³

et al. 2017

JOM

View full text Add to dashboard Cite

The present study considers the identification of crystal plasticity parameters from the knowledge of a deformed microstructure, which has been backtracked to the reference state, and known kinematics along the sample surface. This theoretical question is tackled on a numerical (synthetic) test case. A 2D microstructure with one dimension along the depth is generated, and deformed using known crystal plasticity law. A procedure is proposed to perform the calibration of the constitutive parameters addressing the specific challenge of having a partial knowledge of the boundary conditions. The proposed identification strategy combined with the estimation of the reference microstructure is shown to retrieve the constitutive parameters with good accuracy.

show abstract

Virtual material testing for stamping simulations based on polycrystal plasticity

Cited by 75 publications

References 65 publications

Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: Theory, experiments, applications

Overview of constitutive laws, kinematics, homogenization and multiscale methods in crystal plasticity finite-element modeling: Theory, experiments, applications

Failure Orientation in Stretch Forming and Its Correlation with a Polycrystal Plasticity-Based Material Model for a Collection of Highly Formable Sheet Steels

Backtracking Depth-Resolved Microstructures for Crystal Plasticity Identification—Part 2: Identification

Contact Info

Product

Resources

About