Parameter Identification for Inelastic Constitutive Models

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.