This paper presents an analysis of state-of-the-art of impact detection techniques for aerospace structural components as well as a study about the combination of two promising approaches for localizing an incidental impact event on a typical metallic aerospace structural component as test article. In the aeronautical scenario, some typical damaging events that may occur during service life are runway bird-strike, tool drop and debris impact. The last two cases produce generally high-frequency vibrations that are usually well predicted by ultrasonic techniques. The impacts from birds on the other hand produces vibrations in the lower or modal frequency range. The present work is focused on the possible combination of two methodologies: the first one, related to impacts inducing low-frequency vibrations, is based on the implementation of a Neural Network, while the second one, related to impacts inducing higher-frequency stress waves, is based on an acoustic source localization approach. Both numerical and experimental analyses were implemented on the same isotropic aluminum flat panel, and a possible combination of the experimental sensors arrangement will be discussed within the paper. The results have confirmed the positive performance of the neural network, opening to a more extended experimental campaign mainly oriented to the definition of the system precision, possible fault reconstruction and optimization in the data handling and reduction of computational effort. On the other hand, the main advantage of the acoustic emission formulation is that it does not require the knowledge of the wave velocity profile in the panel. Dependence of the guided wave velocity on the signal frequency for isotropic plates and, also on the wave propagation direction for anisotropic plates are the two major obstacles for acoustic source localization in a plate. Both these obstacles are avoided in this latter formulation.

About the combination of high and low frequency methods for impact detection on aerospace components

Boffa N. D.
;
Arena M.;Monaco E.;Viscardi M.;Ricci F.;Kundu T.
2022

Abstract

This paper presents an analysis of state-of-the-art of impact detection techniques for aerospace structural components as well as a study about the combination of two promising approaches for localizing an incidental impact event on a typical metallic aerospace structural component as test article. In the aeronautical scenario, some typical damaging events that may occur during service life are runway bird-strike, tool drop and debris impact. The last two cases produce generally high-frequency vibrations that are usually well predicted by ultrasonic techniques. The impacts from birds on the other hand produces vibrations in the lower or modal frequency range. The present work is focused on the possible combination of two methodologies: the first one, related to impacts inducing low-frequency vibrations, is based on the implementation of a Neural Network, while the second one, related to impacts inducing higher-frequency stress waves, is based on an acoustic source localization approach. Both numerical and experimental analyses were implemented on the same isotropic aluminum flat panel, and a possible combination of the experimental sensors arrangement will be discussed within the paper. The results have confirmed the positive performance of the neural network, opening to a more extended experimental campaign mainly oriented to the definition of the system precision, possible fault reconstruction and optimization in the data handling and reduction of computational effort. On the other hand, the main advantage of the acoustic emission formulation is that it does not require the knowledge of the wave velocity profile in the panel. Dependence of the guided wave velocity on the signal frequency for isotropic plates and, also on the wave propagation direction for anisotropic plates are the two major obstacles for acoustic source localization in a plate. Both these obstacles are avoided in this latter formulation.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/870238
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