A10.2 Inverse Determination of Elastic Material Parameters from Ultrasonic Guided Waves Dispersion Measurements using Convolutional Neuronal Networks
- SMSI 2021
2021-05-03 - 2021-05-06
- SMSI 2021 - Measurement Science
- A10 Inverse Problems in Measurements (Special Session)
- M. Held, A. Rashwan, M. Lauschkin, J. Bulling, Y. Lugovtsova, J. Prager - Bundesanstalt für Materialforschung und -prüfung, Berlin (Germany)
- 239 - 240
In the context of Industry 4.0 and especially in the field of Structural Health Monitoring, Condition Monitoring and Digital Twins, simulations are becoming more and more important. The exact determination of material parameters is required for realistic results of numerical simulations of the static and dynamic behavior of technical structures. There are many possibilities to determine elastic material parameters. One possibility of non-destructive testing are ultrasonic guided waves. For the evaluation of the measurement results, mostly inverse methods are applied in order to be able to draw conclusions about the elastic material parameters from analysing the ultrasonic guided wave propagation. For the inverse determination of the elastic material parameters with ultrasonic guided waves, several investigations were carried out, e.g. the determination of the isotropic material parameters through the point of zero-groupvelocity  or anisotropic material parameters with a simplex algorithm . These investigations are based on the evaluation of dispersion images. Machine learning and in particular Convolutional Neural Networks (CNN) are one possibility of the automated evaluation from image data, e.g. classification or object recognition problems. This article shows how the dispersive behavior of ultrasonic guided waves and CNNs can be used to determine the isotropic elastic constants of plate-like structures.