Delamination Detection Research Articles

Investigation of real delamination detection in composite structure using air-coupled ultrasonic testing

Air-coupled ultrasonic testing (ACUT) is a non-destructive testing method that has been used to investigate composite defects. In this research, a new technique of creating a delamination defect is presented to produce a thin air gap for measuring realistic ultrasonic responses. Composite samples have been made of fiberglass and epoxy resin with six delamination defects in different sizes. A proper setup test has been established for the through-transmission ultrasonic inspection of the composite samples. For fault detection, statistical parameters are derived from ultrasonic response signals. Following dimensionality reduction, a support vector machine (SVM) classifier has been used to improve delamination detection accuracy. Also, the numerical simulation of ultrasonic testing on the composite specimen with delamination defects has been performed to obtain the test parameters. Although the ultrasonic responses have been significantly weakened by the delamination defects (air gaps) yet applying signal processing techniques following by an SVM classifier has proven to improve delamination detection in the composite.

A combined global-local approach for delamination assessment of composites using vibrational frequencies and FBGs

In this paper, a combined global-local strategy was developed for structural health monitoring (SHM) of fiber reinforced polymer (FRP) composites to assess the internal delamination damage, which has combined vibrational frequency shifts as the global index and the fiber Bragg grating (FBG) wavelength shifts as the local index. The delamination detection was carried out in two steps. The first step was based on the global damage index, which is the changes in multiple modes of frequencies measured by a non-contact scanning laser Doppler vibrometer (SLDV) system. Machine learning (ML) algorithms including support vector machine (SVM) and extreme learning machine (ELM) algorithms were used to initially predict the delamination interface, location, and size through frequency shifts. Numerical and experimental verification results show that SVM has a higher prediction accuracy than ELM when only a small number of samples exist, and the classification of SVM is better than its regression function to be used in the prediction of discrete delamination interfaces. To verify the damaged area predicted by frequency changes and further update the delamination edges with better accuracy, the second step has taken the changes in the wavelength of multiple FBG sensors as the local damage index. Multistage loads were applied at equidistant positions of the FRP specimens to induce deformations and the wavelength shifts in FBGs were used to determine the boundary of delamination. The results showed that delamination in FRP composites can be assessed more precisely using the global-local two-step SHM strategy, which can compensate for the shortcomings of only using vibration-based or FBG-based monitoring techniques. Besides, the SVM algorithm showed excellent predictive performance in both steps and had great potential in delamination damage monitoring.

A methodology for sensor number and placement optimization for vibration-based damage detection of composite structures under model uncertainty

Structural health monitoring techniques for composite structures are dependent on the data acquired from sensors; thus, optimal sensor network design is important to provide adequate and reliable information. This paper presents a novel framework for optimizing the number of sensors and sensor placement under model uncertainty for vibration-based damage detection in composite structures. The number of target vibration modes to be identified is first determined, based on the sum of modal effective mass fractions, to sufficiently capture dynamic responses. Since any point on the structure can be a candidate sensor position (causing a large design space), a modal kinetic-energy-based index is proposed to narrow the design space. Design objectives simultaneously minimize the number of sensors and the mean and standard deviation values for the root mean square error of off-diagonal terms in the modal assurance criterion matrix. The nondominated sorting genetic algorithm II is adopted to solve this problem. Monte Carlo simulation (MCS) is applied to evaluate the latter two objective functions. To reduce computation costs, real performance evaluations in MCS are replaced with Gaussian process regression models. To validate the optimized sensors, an optimization-based delamination detection process is applied. Case studies are presented to demonstrate the developed optimization framework.

Detection of delamination and rebar debonding in concrete structures with ultrasonic SH-waveform tomography

The detection of delamination and rebar debonding is among the most frequently encountered issues for inspecting concrete for quality control and assurance. This paper presents a novel application of a 2D full-waveform inversion of ultrasonic SH-waves (SH-FWI) for detection of delamination and rebar debonding in concrete structures. The ultrasonic data are collected by a commercial shear-wave tomography device (MIRA) and analyzed by the SH-FWI method to extract the material properties (density and S-wave velocity) at mm-resolutions. Tested on a concrete slab with four delaminations of various sizes and depths and rebar debonding, the method was able to characterize all delaminations at accurate location and sizes, as well as identify the existence of rebar debonding. Compared to the synthetic aperture focusing technique (SAFT), the SH-FWI method provided clearer structural images with more detailed information of the delamination and rebar debonding.

Kriging and dimension reduction techniques for delamination detection in composites using electrical resistance tomography

The electrical resistance tomography (ERT) is a non-destructive evaluation (NDE) technique that uses the inherent conductivity variations of composites to characterize internal damage. This article presents a study on the use of Kriging surrogate models for inverse identification of delamination in composites using ERT. Kriging surrogates are used to predict the change in electrical response of composites with delamination and matrix cracking damage. The results show that Kriging surrogates can successfully identify delamination even in the presence of matrix cracking. The technique demonstrated here that combines model order reduction with Kriging surrogates provides a computationally efficient and accurate method for damage identification using ERT.

Autonomous Assessment of Delamination Using Scarce Raw Structural Vibration and Transfer Learning.

Deep learning has helped achieve breakthroughs in a variety of applications; however, the lack of data from faulty states hinders the development of effective and robust diagnostic strategies using deep learning models. This work introduces a transfer learning framework for the autonomous detection, isolation, and quantification of delamination in laminated composites based on scarce low-frequency structural vibration data. Limited response data from an electromechanically coupled simulation model and from experimental testing of laminated composite coupons were encoded into high-resolution time-frequency images using SynchroExtracting Transforms (SETs). The simulated and experimental data were processed through different layers of pretrained deep learning models based on AlexNet, GoogleNet, SqueezeNet, ResNet-18, and VGG-16 to extract low- and high-level autonomous features. The support vector machine (SVM) machine learning algorithm was employed to assess how the identified autonomous features were able to assist in the detection, isolation, and quantification of delamination in laminated composites. The results obtained using these autonomous features were also compared with those obtained using handcrafted statistical features. The obtained results are encouraging and provide a new direction that will allow us to progress in the autonomous damage assessment of laminated composites despite being limited to using raw scarce structural vibration data.

Delamination detection in composite plates using random forests

Laminated composites constitute a crucial family of advanced materials for the development of high-performance components. A classic challenge with this composite family is delamination. Lately, vibration-based methods, augmented by dynamic response signal processing and soft computing techniques, have become central for the non-destructive detection of delamination. To date, several studies have been put forth in relation to tackling the problem. Despite this, the concurrent determination of the presence and severity of delamination remains a challenge for structures more complicated than simple beam-like structures. Grounded on the combination of random forests (RF) and natural frequency-shift damage assessment methods, this paper presents an alternative strategy for the simultaneous predictions of severity and location parameters of delamination in composite plates. The study commences with the establishment of a robust finite element (FE) procedure for the inclusion of delamination in composite plates. Subsequently, the FE procedure was validated with good agreement against published results. With high-dimensional datasets generated from the FE procedure, four RF models with different architectures are developed and compared (including a comparison with a deep neural network model). Relying only on four natural frequencies, the predictive strength of the RF model with optimally tuned hyperparameters is assessed via the FE-generated and experimentally obtained datasets. Results indicate that the proposed RF-based method is capable of accurately predicting five parameters quantifying the location and severity of delamination in composite plates. The best RF model produces a coefficient of correlation as high as 0.996 for the location and size parameters and up to 90% accuracy for classifying the delaminated interface.

Delamination detection in composite laminates using improved surrogate-assisted optimization

Vibration-based delamination detection can be posed as an optimization problem, wherein the discrepancy of the frequency shifts obtained from a numerical model with assumed delamination parameters is minimized from target values of the frequency shifts. Unlike cracks, delamination is characterized by a mix of continuous and discrete parameters. The continuous variables comprise the in-plane locations and sizes, whereas discrete parameters correspond to the interface where the damage occurs. While the recent studies have demonstrated the effectiveness of surrogate-assisted optimization in determining the locations and sizes of the delamination, the prediction of interface has received scarce attention. To improve on this aspect, in this paper, individual surrogate models are built corresponding to each of the interfaces. Furthermore, we also attempt to improve the underlying optimization method by using multiple populations instead of one in order to reduce the likelihood of being trapped in local minima. The performance of the proposed approach is numerically investigated by considering various factors, such as the different number of samples for training the surrogate model, the combinations of different modes of frequency shifts used as input for damage detection, and the addition of artificial noise to the numerical frequency shifts to simulate the unavoidable measurement errors. Experimental investigations are also conducted on six delaminated beam specimens and one intact specimen. The proposed approach showed significant improvements in identifying the damage parameters, and in particular improving the prediction accuracy of the damage interface.

A new high-frequency eddy current technique for detection and imaging of flaws in carbon fibre-reinforced polymer materials

Using a high-frequency (50 kHz-4 MHz) alternating current field measurement (ACFM)-style inspection, non-destructive imaging of carbon fibre-reinforced polymers (CFRPs) was performed for the detection and evaluation of flaws developed during their manufacture or in-service use. This was achieved using quantum well Hall-effect (QWHE) sensors, which have proven suitability for use in different non-destructive testing and evaluation (NDT&E) applications based on their high sensitivity (they require lower strength applied fields and can detect smaller perturbations in the magnetic field), high linearity (high contrast and imaging evaluation capabilities), wide dynamic range (making them less sensitive to offset and lift-off variations), wide frequency operating range (DC to MHz) and compact size (5-70 microns depending on the application). Their advanced III-V semiconductor materials and design enable these characteristics. Their low capacitance allows them to be operated at significantly higher frequencies than coils of comparable sensitivity or size. As such, the inherent advantages of QWHE sensors have been used in conjunction with a high-frequency ACFM-style magnetic imaging inspection technique, which is referred to as quantum well eddy current field measurement (QW-ECFM) in this paper. Here, the fundamentals of this new technique are outlined, as well as the outcomes of such a technique for evaluating CFRP materials, where individual fibre bundles have been resolved in high detail with high contrast. In addition, the ability to detect fibre misalignment has been shown, suggesting technique sensitivity to 3D orientations of fibre for better material qualification and the detection of delamination down to 2 mm in diameter. Therefore, this paper aims to provide an overview of this new QW-ECFM technique and summarise its performance for the detection and evaluation of various CFRP material flaws that are commonly found during manufacture and service.

Application of non-invasive active infrared thermography for delamination detection in fresco

In the most common method of fresco painting, a unique integration of paint and plaster allows for frescos to acquire great durability and permanence. As the plaster or layers of fresco walls deteriorate over time, frescos become vulnerable to serious damage. In order to prevent such damage without harming frescos, non-destructive techniques must be used to analyze and determine areas of structure delamination. In the past, different non-destructive methods have been studied. However, many require expensive equipment. The main scope of this work is the evaluation of the effectiveness of non-invasive detection of fresco delamination via thermography, a comparatively inexpensive technique. In non-invasive active infrared thermography, thermal images are captured of a fresco before, during, and after a heating or cooling process. A defect beneath the surface acts as an insulative pocket, which in turn entraps heat and decreases the rate of heat diffusion. The accumulation of heat results in a defective region being abnormally hotter than a non-defective region, which causes a greater change in temperature throughout a heating or cooling process. Therefore, the analysis of the temperature change highlights defects location and entity beneath the surface. This technique was used and validated with two constructed surrogates, one with a known defect and one without a defect. The thermal analysis of the surrogates was performed via MATLAB®. Additionally, simulations of heating and cooling processes on modeled surrogates were generated in COMSOL® Multiphysics. The results of these simulations assessed the MATLAB® analysis and the use of non-invasive thermography as a tool to detect fresco delaminations. This method was then implemented on frescos within the Senate Reception Room of the United States Capitol Building.

A study of the effects of structural delamination location on delamina-tion detection using a non-linear chaotic oscillator method

This work aims to investigate the effects of structural delamination location on the effectiveness of delamination assessment using a vibration-based non-linear chaotic oscillator method. The change in structural vibration characteristics due to delamination at different structural locations can pose a challenge for accurate delamination detection due to the possible weak changes in the measured vibration signal and the existence of noise that can corrupt the signal. Thus in this work, a chaotic oscillator method was used due to its sensitivity to relatively small changes in measured vibration signal and robustness to measurement noise. The effects of vibration sensing location on the sensitivity in detecting the location of delamination was also investigated in this work. The Lyapunov Exponent was used in conjunction with the chaotic oscillator as a damage index, for the purpose of defining an effective measure to locate the delamination damage in a laminated structure. The correlation between the damage index and vibration sensing location for different delamination locations was investigated for a laminated beam structure, with a method for finding an optimal location for vibration sensors proposed. It was found that a vibration sensor placed in selected structural regions can provide an increased level of sensitivity in detecting certain delamination locations. The results from this work also demonstrated the effectiveness of the developed method in determining an optimal placement for vibration sensors for delamination detection.

An algorithm based on logarithm of wavenumber amplitude for detection of delamination in carbon fiber composite

Delamination is one of the most common types of damage/defect in carbon/glass fiber reinforced composites and many researchers use frequency-wavenumber analysis to detect delamination. There are large fluctuations in amplitudes when Lamb wave propagates in composite plate, so that it is hard to precisely identify the boundary of delamination simply from the change of wavenumber. This paper has presented an algorithm based on logarithm of wavenumber amplitude to detect the delamination in carbon fiber plate with high precision. In wavenumber spectra after logarithm processing, the wavenumber amplitudes at delamination boundary can be significantly amplified and the resulting curve of sum of amplitudes can be used to effectively identify the boundary. In this paper, the principle of the logarithm of amplitude algorithm has been introduced and the effectiveness of the algorithm in delamination identification has been validated by finite element simulations and physical experiments. Compared with conventional algorithms, the proposed algorithm can effectively identify the boundary with better accuracy (less than 5%) and has decent identification stability under different depths of delamination. Our research has great potential for solving the challenge in precise identification of delamination boundary.

Microwave Non-Destructive Testing for Delamination Detection in Layered Composite Pipelines.

Microwave imaging and defectoscopy are promising techniques for dielectric composite evaluation. Their most significant advantage is their relatively high penetration depth. Another feature worth noting is that traditional methods could not acquire an internal content with such a low impact on both the sample and surrounding environment, including the test operator, compared to other techniques. This paper presents microwave non-destructive and noninvasive methods for quality evaluation of layered composite materials using an open-ended waveguide probe. Pure |S11| parameters only exceptionally give a clear answer about the location of material cracks. Therefore, this makes it necessary to analyze these parameters simultaneously along with several other factors, such as stand-off distance, probe type or wave polarization. The purpose of the work was to find the dependency between the physical state of a layered composite powerplant pipeline and the S-matrix parameters response (reflection and transmission parameters) in a Ku frequency band that has not yet been extensively researched. Lower-frequency measurements broaden the application possibility for thicker composites, mainly because of a higher penetration depth and measurement setup availability. Different methods have been shown, including reflection and transmission/reflection methods, both in close proximity and in stand-off distance. The measurements are based on a low-complexity experimental setup.

Application of laser ultrasonic for detecting delamination in Cu/Al composites

A laser ultrasonic method based on guided waves was proposed to characterize delamination defects in Cu/Al composites. Simulation analysis was used to analyse to the propagation characteristics of laser ultrasound. The delamination defects made the mode of laser ultrasound change from surface wave to guided wave. The influences of delamination size on guided waves were analyzed to investigate the interaction between laser ultrasound and delamination. The amplitudes of the guided waves were used to characterize the delamination defect. A laser ultrasound experimental platform was built. The experimental results and simulation analysis results have similar trends and characteristics. The C-scan imaging results can show the location and size of the delamination defects. Therefore, the detection method based on laser ultrasonic technology is effective for delamination detection in Cu/Al composites, which can provide real-time monitoring data for the rolling process.

A two-step method for delamination detection in composite laminates using experience-based learning algorithm

Delamination in composite laminates reduces the structural stiffness and thus causes changes in the vibration responses of the laminates. Therefore, it is feasible to employ dynamic characteristics (such as natural frequencies and mode shapes) for delamination detection by using an optimization method. In the present study, a two-step method is proposed for the delamination detection in composite laminates using an experience-based learning algorithm. In the first step, one-dimensional equivalent through-thickness beam elements are employed to model the composite laminated beam and potential delamination locations are identified. In the second step, a typical three-dimensional finite mesh is utilized for the beam’s modeling and the detailed delamination information (including the delamination location, size, and interface layer) is detected. This two-step method combines the advantages of the two different modeling techniques and is able to significantly reduce the computational cost without reducing detection accuracy. The proposed method is applied for an eight-layer quasi-isotropic symmetric (0/-45/45/90)s composited beam with different delamination situations to verify its effectiveness and robustness. The performance of the two-step method is demonstrated by comparing with the one-step method and other three state-of-the-art algorithms (CMFOA, PSO, and SSA). Moreover, the influence of artificial noise on the accuracy of the detection performance is also investigated. Both numerical and experimental results confirm the superiority of the proposed method for delamination detection in composite laminates especially for the prediction of delamination interface.

Chipless RFID Tags as Microwave Sensors for Delamination Detection in Layered Structures.

Monitoring multilayer dielectric structures for defects, such as disbonds and delaminations, is an ongoing issue in a number of critical applications. Chipless RFID tags can be used to address some of the issues pertinent to detecting these defects. This work explores the use of chipless RFID tags for this application and investigates the effects of tag operating frequency, material thickness, material dielectric properties, and delamination thickness on this approach's sensing capabilities. The benefits and challenges of this approach are discussed in the context of a collection of simulations and measurements using two new tag designs.

Long-Term Numerical Analysis of Subsurface Delamination Detection in Concrete Slabs via Infrared Thermography

One of the concerns about the use of passive Infrared Thermography (IRT) for structural health monitoring (SHM) is the determination of a favorable period to conduct the inspections. This paper investigates the use of numerical simulations to find appropriate periods for IRT-based detection of subsurface damages in concrete bridge slabs under passive heating along a 1 year of time span. A model was built using the Finite Element Method (FEM) and calibrated using the results of a set of thermographic field inspections on a concrete slab sample. The results showed that the numerical simulation properly reproduced the experimental thermographic measurements of the concrete structure under passive heating, allowing the analysis to be extended for a longer testing period. The long-term FEM results demonstrated that the months of spring and summer are the most suitable for passive IRT inspections in this study, with around 17% more detections compared to the autumn and winter periods in Brazil. By enhancing the possibility of using FEM beyond the design stage, we demonstrate that this computation tool can provide support to long-term SHM.

A New Laser Ultrasonic Inspection Method for the Detection of Multiple Delamination Defects.

Delamination is one of the most common types of defects for carbon fiber reinforced plastic (CFRP) composites. The application of laser techniques to detect delamination faces difficulties with ultrasonic wave excitation because of its low thermal conductivity. Much of the research that can be found in the literature has only focused on the detection of a single delamination. In this study, aluminum foil was pasted onto the surface of the composite so that it was vulnerable to ablation and could acquire a usable signal. Using a fully noncontact system with laser excitation at a fixed point and a scanning laser sensor, the effects of different aluminum foil sizes and shapes on the wavefield were studied for the composites; we decided to use a rectangle with 3 mm length and 5 mm width for laser excitation experiments. Wavefield characteristics of the composite plates were analyzed with single- and multi-layered Teflon inserts. Taking the time window for standard ultrasonic testing as a reference, the algorithms for localized wave energy with appropriate time windows are presented for the detection of single and multiple defects. The appropriate time window is meaningful for identifying each delamination defect. The algorithm performs well in delamination detection of the composites with one or multiple Teflon inserts.

Detection of delamination defects inside carbon fiber reinforced plastic laminates by measuring eddy-current loss

Carbon fiber reinforced plastics are likely to experience delamination due to the heterogeneity and anisotropy of the materials. However, the through-thickness conductivity of CFRPs is low, resulting in a weak output signal and large amounts of noise interference of eddy current testing in the detection of delamination. Based on the eddy-current loss theory, a method is proposed to detect delamination inside CFRPs. The method consists of an absolute probe with an 8-shaped coil and a unique signal extraction technique using eddy-current loss. The 8-shaped coil of the proposed probe enables the probe to generate a sufficiently strong vertical eddy-current component at its geometric center. The eddy-current loss on CFRPs is extracted by the parallel inductor-capacitor resonance circuit and micro-power detection module. The experiments show that the sensitivity of the vertical 8-shaped probe to the delamination detection is higher than that of the circular and square probes. Compared with ECT, the proposed method by measuring eddy-current loss for delamination has a lower frequency and less noise. In addition, this paper further explores the application of the proposed method in automatic defect detection. The results show that the proposed method can be effectively applied to the automatic detection and visualization of delamination inside CFRPs.

Detection of delamination in carbon fibre reinforced composite using vibration analysis and artificial neural network

From recent years damage detection in composite structures is given a prior attention to avoid failure and breakdown of the structures. Delamination in composite structures is one of the main reasons for the failure and also affects the properties of the composite structures. Carbon fibre reinforced composite has a wide applications in industries which requires monitoring and detection of delamination for proper functioning. There are various methods such as non-destructive methods, mathematical models etc. to detect the delamination but are quiet costly, time taking and inefficient. In the present investigation, Artificial neural network (ANN) is used to determine delamination defect in carbon fiber reinforced polymer composite. ANN model can solve complex problems. Hence, the ANN model is developed to predict the delamination length and location from the fixed end by analyzing the first three carbon fiber composite structures three natural frequencies. The natural frequencies and mode shapes are obtained using finite element analysis (FEA). It has been found that the ANN can predict delamination length and location in carbon fiber reinforced polymer composite.