The non-destructive testing of austenitic welds using ultrasound plays an important role in the assessment of the structural integrity of safety critical structures. The internal microstructure of these welds is highly scattering and can lead to the obscuration of defects when investigated by traditional imaging algorithms. This paper proposes an alternative objective method for the detection of flaws embedded in austenitic welds based on the singular value decomposition of the time-frequency domain response matrices. The distribution of the singular values is examined in the cases where a flaw exists and where there is no flaw present. A lower threshold on the singular values, specific to austenitic welds, is derived which, when exceeded, indicates the presence of a flaw. The detection criterion is successfully implemented on both synthetic and experimental data. The datasets arising from welds containing a flaw are further interrogated using the decomposition of the time-reversal operator (DORT) method and the total focusing method (TFM), and it is shown that images constructed via the DORT algorithm typically exhibit a higher signal-to-noise ratio than those constructed by the TFM algorithm.
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Thermosonics is a rapid and potentially cost-saving non-destructive testing (NDT) screening technique that can be applied to the identification of cracks in high pressure compressor turbine blades in turbofan engines. The reliability of the thermosonic technique is not well established for inspecting these complex components; in particular the vibrational energy generated within a component during a thermosonic test is often highly non-uniform, leading to the possibility of missing critical defects. The aim of this study was to develop a methodology, using a combination of vibration measurements and finite element analysis (FEA), to model the vibrational energy within a turbine blade in a typical thermosonic inspection scenario. Using a laser vibrometer, the steady-state vibration response (i.e. frequency response) at several locations on a blade was measured and used to identify the prominent peaks in the frequency spectra. These were then used to generate an excitation function for the finite element modelling approach. Acceptable correlation between the measured and simulated vibration response at a number of specific locations on the blade allowed the forcing function to simulate the vibration response across the whole blade. Finally, the predicted displacement field was used to determine the vibrational energy at every point on the blade which was mapped onto a CAD representation of the blade, thereby highlighting areas on the blade that were below the defect detection threshold.
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