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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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The identification of material parameters of constitutive models is based on identification experiments. Since even specimens from the same lot show high deviations in the experimental data, the identification of the material parameters leads to different results for one and the same material. The number of identification experiments is usually not large enough for a statistical analysis of the deviations in the identified parameters. In order to overcome this problem we present a method of stochastic simulation which is based on time series analysis for generating artificial data with the same stochastic behaviour as the experimental data. The stochastic simulations allow an investigation of the confidence in the fits of the material parameters. We validate the stochastic simulations by comparing the results of the parameter identification from experimental data with the results from artificial data. The presented simulation method applied here turns out to be a suitable tool for generating artificial data for various kinds of analysis purposes. However, it is very important to take into account that the machines which perform the experiments do not maintain constant strain rates in the loading history of the tension and cyclic experiments.

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