In this contribution, we study the applicability of non-contact vibration analysis for flaw detection on the example of a ceramic electrolyte cup. These components are key parts in a prospective power cells design. First, extensive numerical modal analysis was performed using finite element modelling (FEM). Beside the complete mode spectrum of the freefloating perfect component, the influence of the suspension as well as deviations from the ideal geometry to the eigenmodes were studied. Additionally, the impact of different defect parameters, such as shape, location, and size, on the eigenmodes was investigated. For experimental investigation a soft suspension, impact excitation pendulum and near-surface microphone array rack were designed and built. Initially the samples with reference geometry and no defects have been measured. Eigenfrequencies, damping ratios and mode shapes have been extracted from the microphone array records using the operational modal analysis (OMA) algorithms, as the impact excitation signal was not traced. Experimental and numerical data have shown the good agreement. Further, the samples with reference defects, induced by laser cuts of different length and position, as well as laser drilled holes were studied. Depending on their type, size and position, the defects lead to a decrease of some eigenfrequencies and to a splitting of formerly degenerate modes. Same effects for a real crack are shown. Based on these results, preliminary application boundaries and potential development patterns for non-contact modal testing using a microphone array for defect detection are discussed.
Many technical processes, e.g. in mechanical engineering, are causing acoustic emission. Acoustic emission (AE) consists of elastic waves, generated by stress changes in a solid. These waves can be detected at the surface of the solid by piezoelectric sensors. Classical methods to characterize acoustic emission signals include detecting and counting single events, describing their energy and frequency properties. The spreading conditions for acoustic waves in solids and the interference of a large number of AE sources lead to quasi-continuous signals from which no individual AE event can be extracted. This is also typical for wire sawing. If AE signals shall be used for online process monitoring, it is necessary to extract signal properties that are correlated with process changes. A common feature is the RMS value of the signal, which is correlated with the energy of AE and was found to be very sensitive to changing process conditions. Other features used are the peak values of the signal and the number of zero crossings. To get more information about the actual state of the observed process, parameters of the statistical distribution of short-time RMS like mean value, variation coefficient and skewness have been tested and their sensitivity to process changes have been investigated. An online monitor has been developed based on a hard- and software concept, adapted to process continuous acoustic emission data, with fast acquisition rates and signal processing.
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