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Investigation of manufacturing effects by strength assessment, NDI and guided waves based SHM in composite plates reinforced with bonded stringers
No full textStatic behavior of a disbonded stringer in a co-infused stiffened panel
No full textStructural Health Monitoring deals mainly with sensorized structures where sensors can be secondary bonded on metallic or composite structural elements. Aerospace structural design must account for Damage Tolerance (DT) of structures. To accomplish the airworthiness, a flawed structure is required to stand the design load without any growth and, eventually, repaired. For metallic materials, the damage tolerance approaches are well-established and rely on the evaluation (theoretically and experimentally) of crack propagation velocity. For composite structures the damage-tolerance design is more challenging as the failures that may occur are of different type, most of the times hidden inside the structure and can grow up to a critical size before the conventional inspection techniques detect them. Within the DT approach one of the showstoppers for the full implementation of adhesive bonds in composites (i.e. stringer-skin connections for stiffened plates) are the airworthiness certification requirements for composite aircraft structures as presented within the FAA Advisory Circular 20-107B. In that document the general methods for substantiating the limit load capacity of any bonded stiffener, the failure of which would result in catastrophic loss of the airplane, are prescribed. Among the suggested methods, the only one really permitting to achieve the optimal bonding efficiency without the addiction of disbond stoppers (i.e. rivets), is a “repeatable and reliable non-destructive inspection techniques ensuring the strength of each joint”. That assumption implies the implementation of a reliable SHM system capable of monitoring the extent of an eventual disbond until it reaches a critical dimension at limit load. This paper will present the preliminary results of a research activity where the authors apply static loads to a stiffened plate made of a skin and a bonded stringer (co-infused) where a disbond “starter” has been included during manufacturing. The plate has been sensorized with a strain gauge system to detect the disbonding evolution during load application, in order to verify the effectiveness within a DT approach
Damage detection of CFRP stiffened panels by using cross-correlated spatially shifted distributed strain sensors
No full textThis paper presents a cross-correlation function-based method applied to a spatially shifted differential strain readout vectors using distributed sensors under backscattering random noise and impact excitations. Structural damage is generated by low/medium energy impact on two aeronautical 24-ply CFRP (carbon fiber reinforced plastic) stiffened panels. Two different drop impact locations, two different sensor layouts and two different post-impact solicitations are provided for a skin-stringer debonding detection and length estimation. The differential signal with respect to an arbitrarily selected grounding is used. Then the effects of noise filtering are evaluated post-processing the differential signal by cross-correlating two strain vectors having one sensor gauge position lag. A Rayleigh backscattering sensing technology, with 5 mm of spatial resolution, is used to log the strain map. The results show a good coherence with respect to the NDI (nondestructive inspection) performed by ultrasonic C-scan (an ultrasonic imaging system) flaw detector
Simulation of waves propagation into composites thin shells by FEM methodologies for training of deep neural networks aimed at damage reconstruction
No full textStructural Health Monitoring (SHM) deals mainly with structures instrumented by secondary bonded or embedded sensors that, acting as both signal generators and receivers, are able to “interrogate” the structure about its “health status”. Sensorised structures appear promising for reducing the maintenance costs and the weight of aerospace composite structures, without any reduction of the safety level required. Much effort has been spent during last years on signal analysis techniques in order to extract from signals provided by the sensors networks many parameters, metrics, and images correlated to damages existence, location and extensions. As in many other technological fields, like medical image diagnostics, deep learning techniques in general and artificial neural networks in particular can be a very powerful instrument for damage patterns reconstruction and selection provided that a sufficient and consistent amount of data related to healthy and damaged configuration of the item under test are available. Within this work explicit finite element analysis has been employed to simulate waves propagation within composite plates with and without delaminations due to impacts. The numerical results have been previously validated with analytical solutions and experimental signals then have been used to populate the data sets necessary for deep learning. This paper will present the preliminary results achieved by the authors
Hybrid structural health monitoring on a composite plate manufactured with automatic fibers placement including embedded fiber Bragg gratings and bonded piezoelectric patches
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