Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
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Identification and validation of material models for forming simulations to match experimental data is a key requirement of complex forming applications in the automotive industry. Besides the fact that these models need more and relatively expensive material data input, a problem is still the reliable fit of all necessary parameters. For the steel grade DX54, an interaction of strain rate and yield locus has been identified, finally leading to different material model calibrations. An even better accuracy of feasibility studies is promoted by using advanced yield locus models for forming simulations. With a new set of specially designed experiments in combination with evolutionary strategies, inverse material parameter identification is realized through minimization of a nonlinear error function. This approach defines a new and powerful method for selection and validation of the adequate material model for industrial simulation.