Elastic and dissipative material properties determination using combination of fem and complex artificial neural networks

The application of the complex artificial neural networks (CANN) to the inverse identification problem of the elastic and dissipative properties of deformable solids is considered. The additional information to the inverse problem is components of the displacement vector measured in a set of points at the solid boundary (positional measurement). This solid performs harmonic oscillations in the first resonant frequency. The process of displacement measurement is simulated using the calculation of finite elements software ANSYS, the building of the amplitude-frequency characteristics (AFC) of the displacement, and of the selection of their values for a set of frequencies (frequency measurement). In the given numerical example, problems on the accurate identification of the elastic modulus, and material quality depending on the number of measure points and their location, as well as on the neural network architecture and the length of the training process performed by the complex-value error back propagation (CBP) algorithm are investigated.