Identification of material parameters for inelastic constitutive models: statistical analysis and design of experiments

Harth, Tobias; Schwan, Stefan; Lehn, Jürgen; Kollmann, Franz

doi:10.1016/j.ijplas.2003.11.001

Cited by 44 publications

(26 citation statements)

References 32 publications

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“…It is demonstrated that the applied methods work appropriately, although up to 17 parameters have to be identified simultaneously. The same integration and optimization methods have been employed before in [22,37,39] for the parameter identification of the Chan, Bodner, and Lindholm model. The constitutive models describe the test data of single experiments accurately.…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, the model responses to the parameter estimates are analyzed and compared with the corresponding stress and strain values of the measured data. As in [22,37] the results are stated exemplary for three reference experiments, namely the creep tests with a duration of 100 hours, tension-relaxation tests at strain rate 10 −3 s −1 , and cyclic tests with strain amplitude 0.5% and strain rate 10 −5 s −1 . The correlation coefficients corresponding to the parameter estimates for each model have also been computed and a discussion of the results can be found at the end of this section.…”

Section: Discussionmentioning

confidence: 99%

“…The predictions of the models are then compared with the measured data. A similar study is performed in [22,37] for the constitutive model of Chan, Bodner, and Lindholm [14].…”

Section: Introductionmentioning

confidence: 94%

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Identification of Material Parameters for Inelastic Constitutive Models Using Stochastic Methods

Harth

Lehn

2007

GAMM-Mitteilungen

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The parameters of a constitutive model are usually identified by minimization of the distance between model response and experimental data. However, measurement failures and differences in the specimens lead to deviations in the determined parameters. In this article we present our results of a study of these uncertainties for two constitutive models of Chaboche-type. The models differ only by a kinematic hardening variable. It turns out, that the kinematic hardening variable proposed by Haupt, Kamlah, and Tsakmakis yields a better description quality than the one of Armstrong and Frederick. For the parameter optimization as well as for the study of the deviations of the fitted parameters we apply stochastic methods. The available test data result from creep tests, tension-relaxation tests and cyclic tests performed on AINSI SS316 stainless steel at 600 O C. Since the amount of test data is too small for a proper statistical analysis we apply a stochastic simulation technique to generate artificial data which exhibit the same stochastic behaviour as the experimental data.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Identification of Material Parameters for Inelastic Constitutive Models Using Stochastic Methods

Harth

Lehn

2007

GAMM-Mitteilungen

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“…Apart from deterministic (gradient-based) optimization methods in this case special applications of stochastic (gradientless) approaches are reported in the literature even for quite complicated material models (see e.g. Harth et al [18]). …”

Section: Introductionmentioning

confidence: 99%

Identification and estimation of constitutive parameters for material laws in elastoplasticity

et al. 2007

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MSC (2000) 49Q12, 74P10, 74S05The paper summarizes the comprehensive experience of the authors in the numerical identification and estimation of parameters for anisotropic elastoplastic material laws analyzing inhomogeneous mechanical fields. Generally, the constitutive model under consideration represents a system of differential and algebraic equations. It distinguishes itself by a strictly thermodynamically consistent foundation, and an identical structure in case of small as well as finite deformations. Different rules for the evolution of internal variables describing various hardening effects are proposed. The nonlinear inverse problem of the identification of material parameters is solved with deterministic optimization methods. Basically, the least squares type objective function contains several local and global variables for the comparison of experimental and numerical data. Within this context, the numerical values of the comparative quantities are calculated either using the local integration of the initial value problem at suitably chosen material points or analyzing the entire initial-boundary value problem (e.g. with the Finite Element Method). In order to obtain the gradient of the objective function within the framework of a sensitivity analysis a semianalytical approach based on the implicit differentiation of the equilibrium conditions and the discretized constitutive relations is presented. The numerical algorithms for the direct as well as the inverse problem are implemented into in-house Finite Element codes. Some examples based on real and numerical experiments analyzing selected constitutive approaches are presented.

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“…They also discussed various types of viscoplastic constitutive models applied to FE problems. Hart et al [38] introduced stochastic methods to describe the influence of scattering test data on the identification of material parameters for the B-P viscoplastic constitutive model. The material parameters were determined for AINSI SS 316 stainless steel at 600˚C on the basis of creep tests, constant strain rate tension tests and cyclic tension-compression tests.…”

Section: Introductionmentioning

confidence: 99%

Constitutive Equations

A-to-Z Guide to Thermodynamics, Heat and Mass Transfer, and Fluids Engineering

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Abstract:The aim of the paper is to propose an FE procedure for static and dynamic nonlinear analysis including elasto-viscoplastic constitutive equations of the Bodner-Partom model. Basic equations of the constitutive model are given with the flow graph used in the FE procedure. The proposed procedure has been applied in the MSC.Marc commercial system with a user-defined UVSCPL subroutine that enables application to a wide range of varied finite elements. A number of simple problems of static and dynamic analysis are presented to show the accuracy of this approach. The numerical simulations are compared with experiments to validate the proposed FE procedure.

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Identification of material parameters for inelastic constitutive models: statistical analysis and design of experiments

Cited by 44 publications

References 32 publications

Identification of Material Parameters for Inelastic Constitutive Models Using Stochastic Methods

Identification of Material Parameters for Inelastic Constitutive Models Using Stochastic Methods

Identification and estimation of constitutive parameters for material laws in elastoplasticity

Constitutive Equations

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