Parameter estimation in active plate structures using gradient optimisation and neural networks

Two non-destructive methods for elastic and piezoelectric parameter estimation in active plate structures with surface bonded piezoelectric patches are presented. The first of these solves the inverse problem through gradient based optimisation techniques, minimising the difference between experimental and numerical finite element eigenfrequencies for a test plate. Such minimisation is conducted with feasible arc interior point algorithm (FAIPA), a non-linear interior point algorithm. The second method relies on building a metamodel of the inverse problem, using artificial neural networks (ANNs). The training data set is obtained through the same numerical model as in the first approach. The simulation of the network is then used with the experimental eigenfrequency data set in order to produce an estimate for the material parameters. Results from both approaches are compared and discussed through a simulated identification.