27 March 2018 Impact detection method for composite winglets based on neural network implementation
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Maintenance tasks and safety aspects represent a strategic role in the managing of the modern aircraft fleets. The demand for reliable techniques for structural health monitoring represent so a key aspect looking forward to new generation aircraft. In particular, the use of more technologically complex materials and manufacturing methods requires anyway more efficient as well as rapid application processes to improve the design strength and service life. Actually, it is necessary to rely on survey instruments, which allow for safeguarding the structural integrity of the aircraft, especially after the wide use of composite structures highly susceptible to non-detected damages as delamination of the ply. In this paper, the authors have investigated the feasibility to implement a neural network-based algorithm to predict the impact event at low frequency, typically due to the bird collision. Relying upon a numerical model, representative of a composite flat panel, the approach has been also experimentally validated. The purpose of the work is therefore the presentation of an innovative application within the Non Destructive Testing field based upon vibration measurements. The aim of the research has been the development of a Non Destructive Test which meets most of the mandatory requirements for effective health monitoring systems while, at the same time, reducing as much as possible the complexity of the data analysis algorithm and the experimental acquisition instrumentation. Future activities will be addressed to test such technique on a more complex aeronautical system.
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Massimo Viscardi, Massimo Viscardi, Maurizio Arena, Maurizio Arena, Pasquale Napolitano, Pasquale Napolitano, } "Impact detection method for composite winglets based on neural network implementation", Proc. SPIE 10599, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, Civil Infrastructure, and Transportation XII, 105990Q (27 March 2018); doi: 10.1117/12.2296571; https://doi.org/10.1117/12.2296571

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