Health Monitoring Of Composite Structures Research Articles

An approach using highly sensitive carbon nanotube adhesive films for crack growth detection under flexural load in composite structures

Sensing capabilities of carbon nanotube (CNT) doped adhesive films under bending conditions were investigated. Standard Mode-II coupons and skin-stringer sub-elements were manufactured while their electrical resistance was monitored. Mode-II energy fracture was improved when adding CNTs in comparison to neat adhesive joints due to their toughening effect. Electrical monitoring showed a stable increase of the electrical resistance with crack length with lower sensitivity than Mode-I testing due to a lower crack opening effect. A good agreement was observed by comparing to theoretical estimations, indicating a uniform adhesive distribution inside the joint, confirmed by SEM analysis. Monitoring tests on skin-stringer sub-elements subjected to bending load showed a similar behavior but some sharp increases were observed, especially in the last stages of the test, due to a less uniform adhesive distribution in the central region. Therefore, the proposed technique shows a high potential and applicability for Structural Health Monitoring of composite structures.

Highly conductive ultra-sensitive SWCNT-coated glass fiber reinforcements for laminate composites structural health monitoring

In the present study, hierarchical single-wall carbon nanotube covered Glass fibres (GF-CNT) are developed for self-sensing Structural Health Monitoring (SHM) of epoxy laminate composites. The unidirectional (UD) CNT-modified glass fibers were fabricated by a versatile and scalable wet-chemical blade coating deposition process under ambient conditions. GF-CNT were employed to manufacture UD damage sensing composite laminates. Scanning Electron microscopy (SEM) revealed an extremely homogeneous CNT nanolayer consisting of highly entangled CNT networks covering fully the GF surfaces. A comprehensive electrical characterization of the manufactured laminate was carried out, revealing a strongly orthotropic response in terms of electrical resistivity. The damage sensing capability of the new developed “smart” reinforcement material was verified taking advantage of mode I Double Cantilever Beam (DCB) tests carried out on specimens with a pre-delamination. The electrical resistance, measured during the tests, exhibited a pronounced increase proportional to the delamination growth. The experimental data were also compared with the assessment of a predictive analytical model, showing a very satisfactory agreement.

Full-field strain prediction using mode shapes measured with digital image correlation

Health and condition monitoring of composite structures are critical in engineering especially in the wind, civil, aviation, and auto industries. However, considering the geometry and size of the structures, analyzing critical locations can become challenging. Traditional sensors such as strain-gauges are widely used to collect operating data, but these conventional methods cannot present full-field data and only show the measurement data at a few discrete locations. Baqersad and Bharadwaj have recently developed a Strain Expansion-Reduction Approach (SERA) to bridge this gap and to expand a limited set of measurements and obtain full-field strain data. This approach uses the strain mode shapes from Finite Element Analysis (FEA) to develop a transformation matrix that expands the limited strain data measured using strain-gauges and predicts full-field strain over the entire structure. However, for many structures, it is challenging to accurately model the geometry or material properties for finite element analysis. Many of these structures are made of composite materials and material modes for these structures might not be readily available. In this paper, we use the strain mode shapes extracted using Digital Image Correlation (DIC) in the expansion process. These mode shapes represent actual properties of the structures. The strain mode shapes for a sample structure of a product can be extracted in a test facility using this approach (e.g., a wind turbine blade or a suspension A-arm). An in situ limited set of measurement can be performed using strain-gauges or fiber optic sensors on the structure. Then, the limited data can be expanded using the strain mode shapes to extract full-field strain results. To demonstrate the merit of the approach, we applied the proposed technique to expand real-time operating data measured using a few strain-gauges mounted to a composite spoiler. Using a transformation matrix generated using the DIC operating deflection shapes, the expansion technique predicted the full field strain on the spoiler. It was shown that the proposed methodology could effectively expand the strain data at limited locations to accurately predict the strain at locations where no sensors were placed.

Cluster analysis of acoustic emission signals and tensile properties of carbon/glass fiber–reinforced hybrid composites

Understanding the damage and failure of carbon/glass epoxy hybrid woven composites under tensile loading based on acoustic emission signals is a challenging task in their practical uses. In this study, an approach based on fuzzy c-means algorithm is proposed to process the acoustic emission signals from tensile loading of composites monitored by combining acoustic emission technology and digital image correlation method. The results show that the acoustic emission signals from tensile loading can be divided into three clusters. The three clusters correspond to three kinds of damage modes including matrix cracking, fiber/matrix debonding, delamination, and fiber breakage. By comparing the acoustic characteristics of these classes, a correlation procedure between the clusters and the damage mechanisms observed is proposed. Meanwhile, it can be found that debonding and fiber break signals for glass fiber correspond to a lower frequency range than that for carbon fiber. Moreover, the method combining acoustic emission and digital image correlation can effectively monitor the damage process of the specimen both on the inside and outside, which can provide a reference for the health monitoring of composite structure.

M3D aerosol jet printed buckypaper multifunctional sensors for composite structural health monitoring

Abstract Sensors placed along various stages of manufacturing processes can identify errors and ensure the quality of a product. Specially, sensors are becoming essential for more complicated composite fabrication due to coupling together heat transport, curing kinetics, resin rheology and flow as well as pressure effects. In this research, embedded composite sensor fabrication, maskless mesoscale materials deposition system has been used to print electrically conductive silver patterns on the carbon nanotube (CNT) sheets or buckypaper (BP) to measure its resistance change, and thus used as embedded sensors in composite laminates for both manufacturing process and structure health monitoring. Three different samples of nanotube sheets were used in this work and compared. The samples exhibited slightly different behaviors in terms of resistive response during the resin infusion and curing process with vacuum bagging, yet also demonstrated similar patterns, which indicated their sensing capabilities. Upon the resin flow, the sensor resistance dropped ∼20–30% from the initial value, then increased up to 120–145% compared to its minimum value at the final curing stage. After curing process, embedded sensors could monitor the impact or structural deformation of a composite part.

Impact paint sensor based on polymer/multi-dimension carbon nano isotopes composites

We presented a novel impact paint sensor made of piezoresistive nano-carbon composites and studied its characteristics. The paint sensors were fabricated with multi-walled carbon nanotube (MWCNT), exfoliated graphite nano-platelets (xGnP), and a hybrid type of the two nano-carbon fillers and were sprayed onto a carbon fiber-reinforced plastic panel for lab testing. In ball drop impact test, the MWCNT-xGnP-based hybrid sensor showed the best characteristics in impact energy sensing within the range 0.07–1.0 J. We also studied the piezoresistive mechanism due to dimensional variations of nano carbon isotopes for sensor design. Piezorestivity of nano-carbon sensor was significantly dominated the electrical contact variation of the electrical fillers in a matrix. This study is expected to provide a feasibility test for designing impact paint sensors with optimized sensitivity for a composite structural health monitoring.

Impact localization system of composite structure based on recurrence quantification analysis by using FBG sensors

Abstract Composite structures are susceptible to the damage induced by low velocity impacts (LVIs), thus the impact localization on composite structures is of great significance in the health monitoring of composite structures and preventing catastrophic failures. In this paper, an impact localization algorithm based on recurrence quantification analysis (RQA) by using Fiber Bragg Grating (FBG) sensors was proposed. Firstly, the response signals of LVIs were qualitatively analyzed by the graphical analysis method of recurrence plot (RP) and the regions of the LVIs were identified by comparing RQA measures including recurrence rate or determinism detected by different FBG sensors to determine which FBG sensors are the nearest. Then, the distances from LVIs to the nearest FBG sensors were estimated by curve fitting of recurrence rate and determinism. Finally, the location of the LVI predicted by recurrence rate and determinism can be obtained by genetic algorithm and be merged as the final results of impact localization according to their distances to the nearest FBG sensors. The impact localization system was established and verified on a composite plate with a 240 mm * 240 mm experiment area. The experiment results show that this impact localization method is effective in predicting the location of impact on composite structure with a low sampling rate interrogation module and small samples, the maximum and average errors are 38.41 mm and 25.27 mm, respectively.

Structural Health Monitoring of Composites with Newly Developed Textile Sensors In Situ

Smart textile structures are promising solution nowadays for in situ structural health monitoring of composite parts that have important place in transportation industry. Composites made from a polymeric matrix and a fibrous reinforcement have been increasingly studied during the last decade. Conductive yarns as textile sensors can be integrated in textile structures by diverse technologies and crucial issue is that sensors integration does not modify their general behavior. In this work interest is focused on the structural health monitoring of textile reinforced thermoplastic composites with newly developed textile sensors in situ based on the conductive polymer complex poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate) (PEDOT:PSS).

Finite element evaluation of the effects of curvature in Lamb waves for composites structural health monitoring

Abstract This work presents the effect of the high curvature to thickness ratio on the characteristics of Lamb Waves propagating over the skins of composite structures. It is accessed how the curvature on composite skins affects the group velocity of the symmetrical (S0) and asymmetrical (A0) wave modes. It is also accessed the gradient of curvature effect, when the wave propagates from a flat to a curved part on the skin. The results are intended to be used for the improvement of structural health monitoring of wings and wind turbine blades. This is accomplished through dynamic explicit linear finite element method simulations of plates with flat and curved parts made of Eglass-epoxy bidirectional laminate. As the skin structures are often designed to withstand torsional loads, the fibers are aligned with 45 degrees from the leading edge (curved region) throughout the analysis. Several skins with different curvature to thickness ratios are generated and simulated. Results and trends are presented and can be used to improve the algorithms for damage detection on those structures. It is shown that the group velocity of the incoming waves change considerably with the presence of curvature, for both main modes of vibration.

Composite panel structural health monitoring and failure analysis under compression using acoustic emission

This paper documents an application of the acoustic emission (AE) method for structural health monitoring (SHM) of an integrally stiffened composite panel. The panel consists of a skin and two T-stringers and represents a typical part of the wing or fuselage structure. Similar design is used in civil structure parts. Quasi-static loading under compression was carried out to determine the initiation of the buckling modes. Two pieces of integral panels were tested up to failure. Both panels were monitored using 9 AE sensors in the same configuration throughout the test. The final stage of the test was recorded with a high-speed camera to obtain a detailed view of failure development. The velocity of the propagated AE elastic waves was determined experimentally before the test based on the best match of the computed and real positions of the Hsu–Nielsen sources. The determined velocity was subsequently used for AE source localization during the compression test. Different failure mechanisms were directly linked to different sources of AE using cluster analysis. Visualization of the AE location clusters was performed via the Kernel density estimation. High-speed camera images confirmed that the location of failure corresponds with the localization of a specific cluster of AE events. The AE method demonstrated the detection capability of the critical spot at 87% of the panel strength in compression. In conclusion, AE is suitable as an SHM method for damage monitoring of aerospace composite structures as well as for applications in civil structures.

Piezoresistive characteristics of CNT fiber-incorporated GFRP composites prepared with diversified fabrication schemes

The present study utilized piezoresistive carbon nanotube (CNT) fibers for the structural health monitoring of composites. In particular, diversified schemes including i) Pre-casted CNT fiber patches embedded into GFRP composites, ii) CNT fibers stitched to one glass fabric of the composites during the fabrication procedure, and iii) CNT fibers attached to the top of the composites, were proposed for incorporation of CNT fibers to the composites. The fabricated samples were subjected to cyclic tensile loadings with constant and increasing amplitudes to assess their piezoresistive characteristics. All types of samples showed sharp increases and decreases in the electrical resistance values, reflecting the applied stress. They also showed good sensing integrity under extended cyclic loadings as well as against loading up to the point of failure. Moreover, the fabricated composites exhibited tensile strengths comparable to those of the control samples, demonstrating feasibility to be used as a sensor of composite structures.

An Innovative Diagnostic Film for Structural Health Monitoring of Metallic and Composite Structures.

A novel lightweight diagnostic film with sensors/actuators and a multiple-path wiring option using inkjet printing was developed. The diagnostic film allows for systematic, accurate, and repeatable sensor placement. Furthermore, the film is highly flexible and adaptable for placement on complex configurations. The film can be attached to the surface of the structure through a uniform secondary boundary procedure or embedded within the composite layup during curing. The surface-mounted film can simply be peeled off for repair or replacement without scratching or damaging the part. The film offers significant weight reduction compared to other available technologies. A set of extreme temperature, altitude, and vibration environment test profiles were carried out following the Radio Technical Commission for Aeronautics (RTCA) DO-160 document to assess the durability and performance of the diagnostic film for onboard application. The diagnostic film was shown to be durable and reliable in withstanding the variable operational and harsh environmental conditions of tests representing the conditions of regional aircraft.

Electrical response of carbon nanotube buckypaper sensor subjected to monotonic tension, cycle tension and temperature

Fibre reinforced polymer composites are a popular alternative to traditional metal alloys. However, their internally occurring damage modes call for strategies to monitor these structures. Nowadays, carbon nanotube buckypaper is usually used as a sensor to monitor the strain and/or damage of composites subjected to tensile and cyclic fatigue loadings. In this work, this result is achieved by measuring the electrical resistance response of the buckypaper sensor which is co-cured with the composites. The test results show that the buckypaper sensor has a very high strain sensitivity in static tensile test and the strain sensing factor can reach about 64.77; moreover, in the fatigue test, although the temperature decreases the resistance of buckypaper sensor, the rate of resistance change still changes linearly with loading and unloading. These studies indicate that buckypaper sensor has a very good stability and synchronisation for strain monitoring and it can meet the needs for health monitoring of composite structures.

A Modified Empirical Wavelet Transform for Acoustic Emission Signal Decomposition in Structural Health Monitoring

The acoustic emission (AE) method is useful for structural health monitoring (SHM) of composite structures due to its high sensitivity and real-time capability. The main challenge, however, is how to classify the AE data into different failure mechanisms because the detected signals are affected by various factors. Empirical wavelet transform (EWT) is a solution for analyzing the multi-component signals and has been used to process the AE data. In order to solve the spectrum separation problem of the AE signals, this paper proposes a novel modified separation method based on local window maxima (LWM) algorithm. It searches the local maxima of the Fourier spectrum in a proper window, and automatically determines the boundaries of spectrum segmentations, which helps to eliminate the impact of noise interference or frequency dispersion in the detected signal and obtain the meaningful empirical modes that are more related to the damage characteristics. Additionally, both simulation signal and AE signal from the composite structures are used to verify the effectiveness of the proposed method. Finally, the experimental results indicate that the proposed method performs better than the original EWT method in identifying different damage mechanisms of composite structures.

Experimental and numerical estimation of the damage level in multilayered composite plates

AbstractThe detection of the internal and surface damage and estimation of the critical delamination size is discussed. The problem is illustrated on examples of glass/epoxy and graphite/epoxy composite plates. Experimental static and fatigue tensile tests were performed for multi‐layered plates with a circular hole and a circular delamination with the passive thermography and the digital image correlation used simultaneously for full‐field monitoring of thermal and mechanical effects. Both techniques are compared and verified in the context of detection and prediction of damage under static and fatigue loads. The technique of critical delamination size calculation is presented on the example of a plate made of a graphite/epoxy material. The computations were performed for bi‐axial compression using the finite element analysis and multiaxial fracture criteria. Different criteria are compared. It was observed that only nonlinear‐interaction criteria are suitable for the analysis of glass/epoxy and graphite/epoxy composite structures. It was also found that the mixed form of all three gap opening modes may occur in a composite plate with internal delamination. The proposed procedure for detection and analysis of damage is applicable in structural health monitoring of composite structures.

Guided wave excitation and propagation in damped composite plates

Guided wave attenuation in composites due to material damping is strong, anisotropic, and cannot be neglected. Material damping is a critical parameter in selection of a particular wave mode for long-range structural health monitoring in composites. In this article, a semi-analytical finite element approach is presented to model guided wave excitation and propagation in damped composite plates. The theoretical framework is formulated using finite element method to describe the material behavior in the thickness direction while assuming analytical expressions in the wave propagation direction along the plate. In the proposed method, the Kelvin–Voigt damping model using a complex frequency-dependent stiffness matrix is utilized to account for anisotropic damping effects of composites. Thus, the existing semi-analytical finite element approach is being extended to include material damping effect. Theoretical predictions are experimentally validated using scanning laser Doppler vibrometer measurements of guided wave propagation generated by a circular piezoelectric wafer active sensor transducer in a unidirectional carbon fiber reinforced polymer composite plate. The proposed method achieves good agreement with the experimental results.

Impact localization for composite plate based on detrended fluctuation analysis and centroid localization algorithm using FBG sensors

Structure health monitoring of composite structures is essential for safety assurance and attracted more attention in recent years. In this paper, a low velocity impact (LVI) monitoring system based on detrended fluctuation analysis (DFA) and centroid localization algorithm is proposed. First, the distance from the LVI to neighboring FBG sensors can be estimated by analyzing the Hurst exponents calculated from response signal of LVI with different distance to monitoring FBG sensors by DFA. Then, the LVI location can be determined by region identification algorithm and centroid localization algorithm. To verify the performance of the proposed LVI monitoring system, a LVI localization system using optical fiber Bragg grating (FBG) sensor was constructed and validated on a CFRP plate with 240 mm*240 mm*3 mm experiment area. The experiment results showed that the average error of localization was 31.5 mm. The proposed localization method based on DFA and centroid localization algorithm demonstrates an effective means of LVI localization.

Clustering of interlaminar and intralaminar damages in laminated composites under indentation loading using Acoustic Emission

This study focuses on the clustering of the indentation-induced interlaminar and intralaminar damages in carbon/epoxy laminated composites using Acoustic Emission (AE) technique. Two quasi-isotropic specimens with layups of [60/0/-60]4S (is named dispersed specimen) and [604/04/-604]S (is named blocked specimen) were fabricated and subjected to a quasi-static indentation loading. The mechanical data, digital camera and ultrasonic C-scan images of the damaged specimens showed different damage evolution behaviors for the blocked and dispersed specimens. Then, the AE signals of the specimens were clustered for tracking the evolution behavior of different damage mechanisms. In order to select a reliable clustering method, the performance of six different clustering methods consisting of k-Means, Genetic k-Means, Fuzzy C-Means, Self-Organizing Map (SOM), Gaussian Mixture Model (GMM), and hierarchical model were compared. The results illustrated that hierarchical model has the best performance in clustering of AE signals. Finally, the evolution behavior of each damage mechanism was investigated by the clustered AE signals with hierarchical model. The results of this study show that using AE technique with an appropriate clustering method such as hierarchical model could be an applicable tool for structural health monitoring of composite structures.

Real-time monitoring of low-velocity impact damage for composite structures with the omnidirection carbon nanotubes’ buckypaper sensors

A novel, omnidirectional, nanomaterial-based sensor technology which can provide wide area damage detection of composite structures was proposed in this work. The behaviors of the buckypaper sensors subjected to both tensile and low-velocity impact were investigated. The experimental results showed that the rectangle buckypaper sensor has a large range of sensing coefficients from 21.40 to 35.83 at different directions under tensile. However, the circular buckypaper sensor has a steady sensing coefficient of about 155.63. Thus, the circular buckypaper sensor as a kind of omnidirectional sensor was chosen to monitor the impact damage. The low-velocity impact damage of composite structures is characterized by the gauge factor of omnidirectional buckypaper sensors and the results of C-scanning. Omnidirectional buckypaper sensors’ electrical resistance increases with repeated impact loading; composite structure elastic deformation and damage evolution can be identified from resistance change. Experiment results show that structure monitoring based on the omnidirectional buckypaper sensor not only can detect small barely visible impact damage flaws and the damage evaluation of composite structures subjected to impact but also can determine the location of low-velocity impact damage through the analysis of results. Through comparison with C-scan, the results have preliminarily demonstrated that the omnidirectional carbon nanotubes’ buckypaper sensor can serve as an efficient tool for sensing the evolution of impact damage as well as serve structural health monitoring of composite structures.

Sparse Principal Component Thermography for Structural Health Monitoring of Composite Structures

Non-destructive testing (NDT) techniques play an important role in structural health monitoring (SHM) of composite structures, among which infrared thermography (IRT) is popular because it is easy to operate, enables rapid inspection of large areas, and presents results as easily interpreted thermal images. In order to achieve noise reduction, feature extraction, and data compression, principal component thermography (PCT) was developed for thermographic data processing. However, each principal component in PCT is a linear combination of all the original pixel values, making the results difficult to interpret and hence affecting defect identification. In this work, sparse principal component thermography (SPCT) is proposed as an improved version of PCT, which provides more interpretable analysis results owing to its structure sparsity and leads to a better defect detection. The feasibility of SPCT is illustrated with two case studies.