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Organisationseinheit der BAM
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
Understanding on how a machine learning model interprets data is a crucial step to verify its reliability and avoid overfitting. While the focus of the scientific community is nowadays orientated towards deep learning approaches, which are considered as black box approaches, this work presents a toolbox that is based on complementary methods of feature extraction and selection, where the classification decisions of the model are transparent and can be physically interpreted. On the example of guided wave benchmark data from the open guided waves platform, where delamination defects were simulated at multiple positions on a carbon fiber reinforced plastic plate under varying temperature conditions, the authors could identify suitable frequencies for further investigations and experiments. Furthermore, the authors presented a realistic validation scenario which ensures that the machine learning model learns global damage characteristics rather than position specific characteristics.
Numerical simulation of ultrasonic guided waves using the scaled boundary finite element method
(2012)
The formulation of the Scaled Boundary Finite Element Method is applied for the computation of dispersion properties of ultrasonic guided waves. The cross-section of the waveguide is discretized in the Finite Element sense, while the direction of propagation is described analytically. A standard eigenvalue problem is derived to compute the wave numbers of propagating modes. This paper focuses on cylindrical waveguides, where only a straight line has to be discretized. Higher-order elements are utilized for the discretization. As examples, dispersion curves are computed for a homogeneous pipe and a layered cylinder.
Lamb waves travel in plates and hollow cylinders over large distances and propagate with multiple mode shapes. Therefore the waves can be used for integrity tests of large scale structures. Each propagating wave mode has a unique dispersive character and a frequency dependent vibration pattern. Therefore, by interacting with flaws each mode is assumed to generate an individual reflection pattern depending on the flaw type, flaw orientation and size. To extract this information a frequency as well as wave number dependent mode excitation is required ensuring a single mode excitation. The challenge, however, is to control the selective mode excitation electronically to allow a sequence of consecutive tests with different wave modes to collect the data. A method is proposed using phased array wedge transducers with fluid coupling for normal force excitation of Lamb wave modes. The excitation principle is investigated analytically on plates by modelling the wave generation in the wedge and the coupling between wedge and structure. The analytical results of the excitation principle are validated by corresponding experiments where the field patterns on the wedge surface and on the plate were scanned using a laser vibrometer. The directivity of the radiated field is investigated together with the direction dependent analysis of the mode content.