Identification of Material Parameters for Inelastic Constitutive Models: Stochastic Simulation

Harth, Tobias; Lehn, Jürgen; Kollmann, Franz

doi:10.1007/978-3-540-36564-8_6

Cited by 3 publications

(11 citation statements)

References 16 publications

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“…In [2] a design of experiments is also performed for a classic Chaboche model with kinematic hardening equations of Armstrong and Frederick and an extended Chaboche model with a kinematic hardening equation of Haupt, Kamlah, and Tsakmakis [4]. Also for these models we obtain optimal designs which consist of a small number of experiments.…”

Section: Resultsmentioning

confidence: 99%

“…Based on these data we developed stochastic simulation models in order to generate artificial data with the same stochastic properties as the experimental data [2,3].…”

Section: Methodsmentioning

confidence: 99%

“…This way the algorithm proceeds until no further improvement is achievable. The applied methods can be found in full detail in [2].…”

Section: Methodsmentioning

confidence: 99%

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Identification of Material Parameters for Inelastic Constitutive Models: Design of Experiments

Harth

2003

Proc Appl Math and Mech

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Section: Resultsmentioning

confidence: 99%

“…Based on these data we developed stochastic simulation models in order to generate artificial data with the same stochastic properties as the experimental data [2,3].…”

Section: Methodsmentioning

confidence: 99%

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Identification of Material Parameters for Inelastic Constitutive Models: Design of Experiments

Harth

2003

Proc Appl Math and Mech

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“…We decided to generate artificial data as described in [21,37], since it is the scatter in these data, which produces the uncertainties in the parameter fits. A validation and robustness study of the stochastic simulation method is given in [20,21]. In [16,32,36] similar applications of stochastic simulations in engineering are given.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, a data base of artificial data can be build up which provides a large enough amount of data. The stochastic simulation method is described and analyzed in detail in [21,37].…”

Section: Introductionmentioning

confidence: 99%

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

Harth

Schwan

Lehn

et al. 2004

International Journal of Plasticity

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Identification of Material Parameters for Inelastic Constitutive Models: Stochastic Simulation

Cited by 3 publications

References 16 publications

Identification of Material Parameters for Inelastic Constitutive Models: Design of Experiments

Identification of Material Parameters for Inelastic Constitutive Models: Design of Experiments

Identification of Material Parameters for Inelastic Constitutive Models Using Stochastic Methods

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

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