Application of evolutionary strategies to industrial forming simulations for the identification and validation of constitutive laws

In the last decade, the deployment of simulation systems in the automotive industry on the basis of the finite element method (FEM) became a standard for the evaluation of sheet metal forming processes. The technical and economic benefits of the FEM based simulation strongly depends on the accuracy of the computed prediction. The predictive capability of FEM based simulations is mainly determined by the chosen physical theory and its numerical solution. This thesis focuses on the application of optimization algorithms for the identification and validation of material models, which belong to the group of the constitutive laws. The objective of this thesis is threefold: Firstly, the identification of potentials regarding the material models in order to maximize the benefit of the FEM forming simulation and, secondly, the development of an identification and validation procedure for material models. Finally, the effect of the deviations between the measured data and the true values of the calibration experiments on the predictive capability of material models is investigate