A stochastic model for parameter identification of adhesive materials

Abstract

In practice, there are only a very limited number of experimental data available. Therefore, the prediction of material behaviour is difficult and a statistical analysis with a stochastic-based method is nearly impossible. In order to increase the number of tests based on experimental data, we apply the method of stochastic simulation based on time series analysis. The generated artificial data have the same stochastic behaviour as the experimental data. Advantages of artificial data are the arbitrary number of data available, and as a conclusion, the process of parameter identification can be statistically analysed. Here, we especially have experiments for adhesive materials for substantial tension tests performed at two different strain rates. Artificial data provide a stochastic proved analysis of the parameter identification concerning distribution and deviations. The analysis shows the possible range of the different material parameters and, therefore, gives a detailed view of the identification process.