The current work details the implementation of a meta-model based correlation technique on a composite UAV wing
test piece and associated finite element (FE) model. This method involves training polynomial models to emulate the FE
input-output behavior and then using numerical optimization to produce a set of correlated parameters which can be
returned to the FE model. After discussions about the practical implementation, the technique is validated on a
composite plate structure and then applied to the UAV wing structure, where it is furthermore compared to a more
traditional Newton-Raphson technique which iteratively uses first-order Taylor-series sensitivity. The experimental testpiece
wing comprises two graphite/epoxy prepreg and Nomex honeycomb co-cured skins and two prepreg spars bonded
together in a secondary process. MSC.Nastran FE models of the four structural components are correlated
independently, using modal frequencies as correlation features, before being joined together into the assembled
structure and compared to experimentally measured frequencies from the assembled wing in a cantilever configuration.
Results show that significant improvements can be made to the assembled model fidelity, with the meta-model
procedure producing slightly superior results to Newton-Raphson iteration. Final evaluation of component correlation
using the assembled wing comparison showed worse results for each correlation technique, with the meta-model
technique worse overall. This can be most likely be attributed to difficultly in correlating the open-section spars;
however, there is also some question about non-unique update variable combinations in the current configuration, which
lead correlation away from physically probably values.
In order to facilitate damage detection and structural health monitoring (SHM) research for composite unmanned aerial
vehicles (UAV) a specialized test-bed has been developed. This test-bed consists of four 2.61 m all-composite test-pieces
emulating composite UAV wings, a series of detailed finite element models of the test-pieces and their
components, and a dynamic testing setup including a mount for simulating the cantilevered operation configuration of
real wings. Two of the wings will have bondline damage built in; one undamaged and one damaged wing will also be
fitted with a range of embedded and attached sensors-piezoelectric patches, fiber-optics, and accelerometers. These
sensors will allow collection of realistic data; combined with further modal testing they will allow comparison of the
physical impact of the sensors on the structure compared to the damage-induced variation, evaluation of the sensors for
implementation in an operational structure, and damage detection algorithm validation. At the present time the pieces
for four wings have been fabricated and modally tested and one wing has been fully assembled and re-tested in a
cantilever configuration. The component part and assembled wing finite element models, created for MSC.Nastran,
have been correlated to their respective structures using the modal information. This paper details the design and
manufacturing of the test-pieces, the finite element model construction, and the dynamic testing setup. Measured natural
frequencies and mode shapes for the assembled cantilevered wing are reported, along with finite element model
undamaged modal response, and response with a small disbond at the root of the top main spar-skin bondline.
The monitoring of adhesively-bonded joints through the use of ultrasonic guided waves is the general topic of this paper. Specifically, composite-to-composite joints representative of the wing skin-to-spar bonds of Unmanned Aerial Vehicles (UAVs) are examined. This research is the first step towards the development of an on-board structural health monitoring system for UAV wings based on integrated ultrasonic sensors. The study investigates two different lay-ups for the wing skin and two different types of bond defects, namely poorly-cured adhesive and disbonded interfaces. The guided wave propagation problem is studied numerically by a semi-analytical finite element method that accounts for viscoelastic damping, and experimentally by utilizing macro fiber composite (MFC) transducers which are inexpensive, flexible, highly robust, and viable candidates for application in on-board monitoring systems. Based upon change in energy transmission, the presence of damage is successfully identified through features extracted in both the time domain and discrete wavelet transform domain. A unique "passive" version of the diagnostic system is also demonstrated experimentally, whereby MFC sensors are utilized for detecting and locating simulated active damage in an aluminum plate. By exploiting the directivity behavior of MFC sensors, a damage location algorithm which is independent of wave speed is developed. Application of this approach in CFRP components may alleviate difficulties associated with damage location in highly anisotropic systems.
Carbon-fiber-reinforced-polymer (CFRP) composites represent the future for advanced lightweight aerospace structures. However, reliable and cost-effective techniques for structural health monitoring (SHM) are needed. Modal and vibration-based analysis, when combined with validated finite element (FE) models, can provide a key tool for SHM. Finite element models, however, can easily give spurious and misleading results if not finely tuned and validated. These problems are amplified in complex structures with numerous joints and interfaces. A small series of all-composite test pieces emulating wings from a lightweight all-composite Unmanned Aerial Vehicle (UAV) have been developed to support damage detection and SHM research. Each wing comprises two CFRP prepreg and Nomex honeycomb co-cured skins and two CFRP prepreg spars bonded together in a secondary process using a structural adhesive to form the complete wings. The first of the set is fully healthy while the rest have damage in the form of disbonds built into the main spar-skin bondline. Detailed FE models were created of the four structural components and the assembled structure. Each wing component piece was subjected to modal characterization via vibration testing using a shaker and scanning laser Doppler vibrometer before assembly. These results were then used to correlate the FE model on a component-basis, through fitting and optimization of polynomial meta-models. Assembling and testing the full wing provided subsequent data that was used to validate the numerical model of the entire structure, assembled from the correlated component models. The correlation process led to the following average percent improvement between experimental and FE frequencies of the first 20 modes for each piece: top skin 10.98%, bottom skin 45.62%, main spar 25.56%, aft spar 10.79%. The assembled wing model with no further correlation showed an improvement of 32.60%.
Composite sandwich structures are important as structural components in modern lightweight aircraft, but are susceptible to catastrophic failure without obvious forewarning. Internal damage, such as disbonding between skin and core, is detrimental to the structures' strength and integrity and thus must be detected before reaching critical levels. However, highly directional low density cores, such as Nomex honeycomb, make the task of damage detection and health monitoring difficult. One possible method for detecting damage in composite sandwich structures, which seems to have received very little research attention, is analysis of global modal parameters. This study will investigate the viability of modal analysis techniques for detecting skin-core disbonds in carbon fiber-Nomex honeycomb sandwich panels through laboratory testing. A series of carbon fiber prepreg and Nomex honeycomb sandwich panels-representative of structural components used in lightweight composite airframes-were fabricated by means of autoclave co-cure. All panels were of equal dimensions and two were made with predetermined sizes of disbonded areas, created by substituting areas of Teflon release film in place of epoxy film adhesive during the cure. A laser vibrometer was used to capture frequency response functions (FRF) of all panels, and then real and imaginary FRFs at different locations on each plate and operating shapes for each plate were compared. Preliminary results suggest that vibration-based techniques hold promise for damage detection of composite sandwich structures.
A new method for signal processing of non-linear and non-stationary data, the Hilbert Huang Transform (HHT), offers insight into signals that cannot be achieved using conventional methods such as the Fourier transform and wavelet decompositions. This study investigates HHT as a potential tool for damage detection, to be eventually incorporated into a realistic structural health monitoring system. After a review of the method and supporting research, an analytical study begins to offer insight into the behavior of HHT. First, HHT is performed on simple sinusoid signals to see how it separates frequency content, then on the same signal with noise added. Finally HHT is performed on data generated from a numerical 8-degree-of-freedom mass-spring system with random burst excitation, where the stiffness of one spring can be decreased to simulate damage. The study finds that there is considerable variability associated with the implementation of HHT. Many variables, such as sampling frequency and overlapping frequencies, can affect the output of HHT drastically, possibly detracting from the value of the result. The method may, however, automatically separate noise from a signal. Application of HHT to the mass-spring system showed that there was only a noticeable pattern to the effect of damage when that damage became severe. However, the implementation process used may need to be more refined before final assessment can be made. Generally, the variability of HHT needs to be better quantified and understood so that a damage index can be derived based on the unique information that the method provides from signals. If this is possible, HHT could be an important tool for structural damage detection and health monitoring.
Unmanned Aerial Vehicles (UAVs) are being increasingly used in military as well as civil applications. A critical part of the structure is the adhesive bond between the wing skin and the supporting spar. If not detected early, bond defects originating during manufacturing or in service flight can lead to inefficient flight performance and eventual global failure. This paper will present results from a bond inspection system based on attached piezoelectric disks probing the skin-to-spar bondline with ultrasonic guided waves in the hundreds of kilohertz range. The test components were CFRP composite panels of two different fiber layups bonded to a CFRP composite tube using epoxy adhesive. Three types of bond conditions were simulated, namely regions of poor cohesive strength, regions with localized disbonds and well bonded regions. The root mean square and variance of the received time-domain signals and their discrete wavelet decompositions were computed for the dominant modes propagating through the various bond regions in two different inspection configurations. Semi-analytical finite element analysis of the bonded multilayer joint was also carried out to identify and predict the sensitivity of the predominant carrier modes to the different bond defects. Emphasis of this research is based upon designing a built-in system for monitoring the structural integrity of bonded joints in UAVs and other aerospace structures.