On the simultaneous estimation of model parameters used in constitutive laws for inelastic material behaviour

Bruhns, Otto T.; Anding, Dirk

doi:10.1016/s0749-6419(99)00046-7

Cited by 42 publications

(24 citation statements)

References 29 publications

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“…Thus, for large a 2 the parameter c 2 must be large as well (and vice versa) in order to reach the same limit. For the extended Chaboche model this correlation is less than it is for the Chaboche model, since instead of the parameter a 2 we have a term containing another inner variable as described in equation (9). A similar explanation as above for the kinematic hardening can be given for the negative correlation of the parameters b and q of the isotropic hardening equation.…”

Section: Analysis Of the Extended Chaboche Modelmentioning

confidence: 87%

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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: Analysis Of the Extended Chaboche Modelmentioning

confidence: 87%

“…We present the algorithm PRINO which consists of the global cluster-oriented method of Price [35] in combination with the local optimization strategy of Müller, Nollau, and Polovinkin [33]. More research about parameter identification can be found e. g. in [4,9,26,28,30,31].…”

Section: Introductionmentioning

confidence: 99%

Identification of Material Parameters for Inelastic Constitutive Models Using Stochastic Methods

Harth

Lehn

2007

GAMM-Mitteilungen

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“…; F N Þ (2) where F represents the multi-objective function and F i ¼ F i (x 1 ,x 2 ,.,x n ) are the various objective functions with x i the n design variables. In particular the MOO function is define as…”

Section: Discussionmentioning

confidence: 99%

“…One of the most important works on this topic is [1]. A specific treatment was developed according to the choice of the material model [2,3], the type of the experimental tests [4,5], the FEM code for the numerical solution and the algorithm on which the optimization was based on [6,7]. A lot of these works are related with the solution of problems in which the dynamic component could be relevant.…”

Section: Introductionmentioning

confidence: 99%

Parameters identification in strain-rate and thermal sensitive visco-plastic material model for an alumina dispersion strengthened copper

Scapin

Peroni

2012

International Journal of Impact Engineering

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a b s t r a c tThe main objective of this paper is getting strain-hardening, thermal and strain-rate parameters for a material model in order to correctly reproduce the deformation process that occurs in high strain-rate scenario, in which the material reaches also high levels of plastic deformation and temperature. In particular, in this work the numerical inverse method is applied to extract material strength parameters from experimental data obtained via mechanical tests at different strain-rates (from quasi-static loading to high strain-rate) and temperatures (between 20 C and 1000 C) for an alumina dispersion strengthened copper material, which commercial name is GLIDCOP Ò . Thanks to its properties GLIDCOP Ò finds several applications in particle accelerator technologies, where problems of thermal management, combined with structural requirements, play a key role. Currently, it is used for the construction of structural and functional parts of the particle beam collimation system. Since the extreme condition in which the material could operate, it is fundamental to characterize it in a wide range both in strain-rate and temperature.The numerical inverse method used in this work is particularly useful to reproduce experimental results when the stressestrain fields in the specimen cannot be correctly described via analytical models. Furthermore this procedure is useful to take into account thermal phenomena generally affecting high strain-rate tests in which the heat conversion of plastic work produces an adiabatic overheating. So, the applicability of this method is particularly indicated in special fields, such as aerospace engineering, ballistic, crashworthiness studies or particle accelerator technologies.The attention is focused on evaluating the most suitable strategy of material model parameters optimization to obtain the best fit between experimental data and numerical results. In this regards, it is important to determine which material model coefficients can be considered as optimization variables and for each of them the most suitable range of variation.

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“…Constitutive relations contain material parameters or material functions which, in general, are not measurable directly but can only be identified analyzing their effects on the evolution of mechanical variables by an optimal numerical adaptation of experimental results. This illposed inverse problem is solved using nonlinear optimization procedures (for an overview see Bruhns and Anding [7], Steck et al [42], and others).…”

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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On the simultaneous estimation of model parameters used in constitutive laws for inelastic material behaviour

Cited by 42 publications

References 29 publications

Identification of Material Parameters for Inelastic Constitutive Models Using Stochastic Methods

Identification of Material Parameters for Inelastic Constitutive Models Using Stochastic Methods

Parameters identification in strain-rate and thermal sensitive visco-plastic material model for an alumina dispersion strengthened copper

Identification and estimation of constitutive parameters for material laws in elastoplasticity

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