Delamination Detection Research Articles

Sol–Gel Waveguide-Based Sensor for Structural Health Monitoring on Large Surfaces in Aerospace Domain

The potential of sol–gel-based optical sensors is investigated for applications in the aerospace domain. To this aim, a low-cost and non-intrusive sol–gel sensor based on waveguides, arranged as a 2D matrix structure, is fabricated by UV photolithography for delamination and damage detection. Two different organic–inorganic sol–gels were selected to fabricate the photonic device: TiO2–SiO2 and ZrO2–SiO2, acting as the waveguide core and the cladding, respectively. A systematic study was performed to determine the manufacturing parameters controlling their properties. The results show that large surfaces can be functionalized via sol–gel methods using the direct laser-writing approach. The structures are characterized in terms of refractive index, and the guiding properties were investigated through simulations and experiments, indicating an excellent behavior regarding the light guidance in a straight waveguide or in the 2D matrix structure grid. Additionally, preliminary tests show that the presence of impact can be easily detected after damage through the induced optical losses on large surfaces. This proof of concept sensor is a promising tool for structural health monitoring. To achieve the ultimate goal, the integration of this photonic sensor will be later performed on aircraft wings.

Nondestructive Testing of Defects in Polymer–Matrix Composite Materials for Marine Applications Using Terahertz Waves

Fibreglass components employed in the marine environment are susceptible to moisture ingress, resulting in a degradation of mechanical properties. The nondestructive testing and evaluation of such materials using acoustic methods is possible under certain conditions, but the detection of internal voids and delaminations is masked by the presence of water in such internal flaws. Herein, we present an investigation into the use of terahertz technology to overcome these limitations, for the detection of damage and water ingress in thick woven glass-fibre composites. This investigation is facilitated by terahertz time-domain spectroscopy, coupled with physical raster scanning to realize object-penetrating imaging. Air-filled defects within the fibreglass volume are clearly identified using this technique. A spectroscopic investigation on the alteration of the terahertz-range dielectric properties of the fibreglass material due to water ingress is performed and a small change is measurable, although likely to be obfuscated by the interaction of terahertz-frequency radiation with the internal fibreglass structure. Moisture-ridden laminates do not impede propagation of terahertz radiation, and therefore wetted materials may be inspected for volumetric defects. A simulation of water-filled volumetric defects shows promise for practical application.

Vibration-based delamination evaluation in GFRP composite beams using ANN

Delamination is definitely an important topic in the area of composite structures as it progressively worsens the mechanical performance of fiber-reinforced polymer composite structures in its service period. The detection and severity analysis of delaminations in engineering areas like the aviation industry is vital for safety and economic considerations. The existence of delaminations varies the vibration characteristics such as natural frequencies, mode shapes, etc. of composites and hence this indication can be effectively used for locating and quantifying the delaminations. The changes in vibration characteristics are considered as inputs for the inverse problem to determine the location and size of delaminations. In this paper Artificial Neural Network (ANN) is used for delamination evaluationof glass fiber-reinforced composite beams using natural frequency as typical vibration parameter. The Finite Element Analysis is used for generating the required dataset for ANN. The frequency-based delamination prediction technique is validated by finite element models and experimental modal analysis. The results indicate that the ANN-based back propagation algorithm can predict the location and size of delaminations in composites with good accuracy for numerical natural frequency data but the accuracy is comparitivelyless for experimental natural frequency data.

Non-destructive inspection and quantification of defects in plywood using a portable digital holographic camera

The demand for plywood products for the interior and exterior construction has grown significantly over the years. Therefore, detection of defects present in the plywood is of prime importance to maintain good quality of construction. In this study, qualitative and quantitative analyses of defects in plywood using digital holographic interferometric technique are presented. In this process, thermal and mechanical loads are applied to the test sample of plywood to generate interferometric fringes by comparing the normal and stressed states of the test sample. A portable digital holographic camera developed for non-destructive testing applications is employed for detection of cracks, delamination, edge defects, and layer opening in the plywood. The defects in the plywood sample are identified by observing discontinuities in the interferometric fringes. Quantitative analysis of defects is performed by plotting their 2-D and 3-D phase profiles from the digital holographic interferometric results. The obtained results of defects on plywood are validated by a mechanical profiler.

Application of Teager-Kaiser's Instantaneous Frequency for Detection of Delamination in FRP Composite Materials.

Composite materials are widely used in many engineering applications and fields of technology. One of the main defects, which occur in fiber-reinforced composite materials, is delamination. It manifests itself in the separation of layers of material and the damaged structure once subjected to mechanical loads degrades further. Delamination results in lower stiffness and the decrease of structure’s carry load capability. Its early detection is one of the tasks of non-invasive structural health monitoring of layered composite materials. This publication discusses a new method for delamination detection in fiber-reinforced composite materials. The approach is based on analysis of energy signal, calculated with Teager–Kaiser energy operator, and comparison of change of the weighted instantaneous frequency for measurement points located in- and outside of delamination area. First, applicability of the developed method was tested using simple models of vibration signals, reflecting considered phenomena. Next, the authors’ weighted instantaneous frequency was applied for detection of deamination using signals obtained from FEM simulated response of the cantilever beam. Finally, the methods effectiveness were tested involving real experimental signals collected by the laser Doppler vibrometer (LVD) sensor measuring vibrations of the delaminated glass-epoxy specimens.

Microwave defectoscopy – detection of composite delamination

This paper is a short description of microwave non-destructive testing methods for composite material and presentation of a simple broadband microwave test method with an open-ended waveguide antenna for the purpose of non-destructive testing and quality evaluation of layered composite materials. Low-complexity experimental setup is shown. Research is aimed to determine the physical state of layered composite material especially in industrial pipelines. The overall applicability of the proposed system for detection purpose is demonstrated with use of experimental results. Suggestions for future researches is the need to develop more sophisticated waveguides systems to increase the scan resolution.

Health Monitoring of RAM-Coated Structures by Active Microwave Thermography

In this article, the application of active microwave thermography (AMT) is investigated as an efficient nondestructive technique (NDT) for health monitoring of structures coated with radio frequency absorbing materials (RAMs). The uniqueness of AMT for this particular application is the use of microwave excitation, which acts as a highly efficient thermal source due to the inherent absorptive electromagnetic properties of RAM-coated structures. In addition, the microwave-induced heating takes place subsurface within the absorbing material of an RAM-coated structure, as opposed to the sole surface heating of conventional thermography. Specifically, this work focuses on detection of delamination, a common defect in carbon fiber reinforced polymer (CFRP) structures, via AMT. To this end, a formulation and analysis method based on electromagnetic/thermal modeling is presented, which enables estimation of the output of an AMT inspection of an RAM-coated CFRP structure suffering from delamination. The effect of the structure’s constitutive properties, the microwave excitation parameters, and the delamination size and depth on its detectability is investigated. In addition, several simulated and experimental results are provided, which verifies the accuracy of the proposed approach and shows the high efficiency of AMT for detection of delamination in such structures.

Detection of Fine Delamination in Glass Fiber Reinforced Polymer Analyzing Full Width Half Maximum of Superimposed Terahertz Signal

Detection of Fine Delamination in Glass Fiber Reinforced Polymer Analyzing Full Width Half Maximum of Superimposed Terahertz Signal

IFFT-Based Microwave Non-Destructive Testing for Delamination Detection and Thickness Estimation

Microwave Non-Destructive Testing (NDT) techniques for dielectric coatings are vital processes in many industrial applications due to their superior defect detection capabilities over traditional NDT methods. However, the limited bandwidth and post-processing complexities of these techniques cause them to fall short of accurately estimating the delamination thickness in such structures. In this paper, a novel low-complexity signal processing method is proposed to estimate the delamination thickness in metal-backed dielectric coatings using bandwidth-limited Open-Ended Rectangular Waveguide (OERW) probes. Here, Inverse Fast Fourier Transform (IFFT) process is used to convert the measured complex reflection coefficients at the surface of the coating to the time domain. Next, the amplitude of a specific time step is used to accurately estimate the delamination thickness without extra time-consuming processing. Using a 3 mm-thick macor samples with machined defects, the proposed technique is validated by correctly estimating delamination thickness down to 0.4 mm using an OERW probe with 13.5 GHz bandwidth. Hence, this technique overcomes the challenge of the OERW's limited bandwidth, which highly affects the possibility of tracking shifting in the peak reflection in the time domain. Moreover, the proposed method does not add to the complexity of the NDT process, which makes it suitable for in-situ real time applications. Hence, following this approach would be of great importance to numerous industries, where accurate thickness estimation of minute delamination in coatings is essential to avoid system failure.

Improved current injection pattern for the detection of delaminations in carbon fiber reinforced polymer plates using electrical impedance tomography

In this article, we introduce a new current injection pattern for electrical impedance tomography. The pattern improves the quality of hole detection in carbon fiber reinforced polymer plates and allows the detection of delaminations. The new pattern is described in detail and compared to three widely used, classical injection patterns. The advantages of the new pattern are demonstrated by numerical finite element analyses for three test cases: a hole of 10-mm diameter, two simultaneous holes, and an ideal delamination in a circular region with a 50-mm diameter. The results are validated experimentally by comparing electrical impedance tomography measurements of a carbon fiber reinforced polymer plate with at first one, then two holes with a 10-mm diameter using classical patterns and the new pattern.

Full wavefield processing by using FCN for delamination detection

A novel full wavefield processing method by using fully convolutional neural networks is presented. The full wavefield of propagating Lamb waves in the fibre-reinforced composite plate was simulated by the parallel spectral element method. It resembles a full wavefield measurements acquired on a surface of the plate by the scanning laser Doppler vibrometer. The aim of the proposed technique is an identification of delamination location, size and shape. It is achieved by pixel-wise image segmentation by using the end-to-end approach. It is possible because of the large dataset of Lamb wave propagation patterns resulting from interaction with delaminations of random location, size and shape. It is demonstrated that the proposed method, tested on numerical data, is performing better than conventional adaptive wavenumber filtering method which was developed in previous work. Moreover, it enables better automation of delamination identification so that the damage map can be created without user intervention. The method was also tested on experimental data acquired on the surface of the specimen in which delamination was artificially created by a Teflon insert. The obtained results with the deep learning approach show its capability to predict the delamination in the numerically generated dataset with high accuracy compared to the conventional damage detection approach. Furthermore, the deep learning model shows the ability to generalize to a further experiential set.

In-situ monitoring and detection of spatter agglomeration and delamination during laser-based powder bed fusion of Invar 36

Invar 36 (FeNi36) is a weldable nickel alloy, known for its low thermal expansion properties, that contains 36% of nickel and 64% iron. The laser-based powder bed fusion process is often associated with defects and flaws that should be controlled for production quality. Spatter formation and delamination are two phenomena that could be observed in thermal images taken during the laser melting process. This paper presents an in-situ monitoring setup for detecting spatters and delamination of Invar 36. The size, shape, count, and cooling rate of spatter particles are evaluated using a combination of infrared pyrometer and high-speed infrared thermography. Thermograms of Invar 36 are processed to determine the spatter characteristics. The influence of processing conditions on the spatter count, spatter size, and size of melt track is discussed. The effect of spatters on the surface morphology of Invar 36 parts is also investigated. The processing conditions that cause melt pool instabilities, excessive spattering, and delamination are presented. This paper concludes recommendations for avoiding delamination and excessive spatter formation during the laser-based powder bed fusion of Invar 36.

Delamination imaging in laminated composite plates using 2D wavelet analysis of guided wavefields

Delamination is a type of damage frequently occurring in laminated composite plates. Delamination detection is crucial to ensure structural integrity and safety. This paper presents a delamination imaging algorithm based on the space-wavenumber analysis of guided wavefields using two-dimensional (2D) continuous wavelet transform (CWT). The premise of this algorithm is that delaminations in a structure will alter wavenumbers in the delamination regions. The state of the art uses the short-space Fourier transform (SSFT) for local wavenumber analysis in order to capture these wavenumber alterations. However, window size in local wavenumber analysis using SSFT needs to be determined according to the wavelength in the delamination regions, which is not known a priori. Moreover, window size in SSFT is fixed and cannot adapt to the wavenumber alterations in varying damage scenarios. To address these disadvantages, the 2D CWT is introduced to analyze the guided wavefield data in the space-wavenumber domain leading to 2D CWT-wavefields. The wavenumber alterations induced by delaminations are well isolated in the 2D CWT-wavefields at fine scales. Window size in wavelet analysis is flexibly adapted to guided wavefields from varying damage cases by adjusting wavelet scale factor that is seen as the pseudo-wavenumber. A final damage map is developed by fusing the 2D CWT-wavefield images. The algorithm is successfully used in detecting multiple delaminations in a GFRP plate with the accurate location, size, and shape of each delamination clearly visualized in the final damage map.

Laser induced graphene for in-situ ballistic impact damage and delamination detection in aramid fiber reinforced composites

Aramid fiber reinforced polymer composites have been shown to exhibit impressive mechanical properties, including high strength-to-weight ratio, excellent abrasion resistance, and exceptional ballistic performance. For these reasons, aramid composites have been heavily used in high impact loading environments where ballistic properties are vital. In-situ damage monitoring of aramid composites under dynamic loading conditions typically requires externally bonded sensors, which add bulk and are limited by size and space constraints. To overcome these limitations, this work presents a piezoresistive laser induced graphene (LIG) interface for embedded impact sensing in aramid fiber reinforced composites. Through the monitoring of electrical impedance during ballistic impact, information regarding time and severity of the impact is obtained. The impact velocity correlates with the impedance change of the composites, due to delamination between aramid plies and damage to the LIG interface. The delamination length in Mode I specimens also correlates to changes in electrical impedance of the composite. The interlaminar fracture toughness and areal-density-specific V50 of the LIG aramid composites increased relative to untreated aramid composites. This work demonstrates a methodology to form multifunctional aramid-based composites with a LIG interface that provides both improved toughness and imbedded sensing of impact and damage severity during ballistic impact.

Delamination detection on a concrete bridge deck using impact echo scanning

AbstractIn this large‐scale field study we use a prototype impact‐echo scanner to detect delaminations on a concrete bridge deck surveying in total over 17,000 m2. Delaminated bridge sections are known from manual sounding and coring. A large‐scale damage assessment is necessary to identify the need of repair work. Based on first results, two lanes of the bridge are repaired and in subsequent tests the bonding of fresh and old concrete is examined. It shows that delaminations found on the bridge deck surface are unevenly distributed with more defects on the southbound lanes. This indicates constructional problems during the concrete placement. The developed scanner excites stress waves by dropping steel solenoids on the surface and recording the impact‐echo frequency by air‐coupled microphone arrays. It is pushed at a speed of 600 m/h, hitting the surface 300 times per square meter. The recorded data is preprocessed on site with an automated delamination detection threshold implemented. Found delaminations are transferred to a bridge map to allow an overall damage assessment.

Resonant Method for Detection and Identification of Delamination in Composite Plates by Elastic Guided Waves

By the example of a local inhomogeneity in the form of a horizontal crack, a numerical and experimental confirmation of the existence of resonance effects in an inhomogeneous elastic strip, investigated by I. I. Vorovich, is given. Examples of the use of this phenomenon for detecting delamination in composite plates and determining their size and depth based on the data of ultrasonic monitoring by traveling Lamb waves are given.

Non-contact detection of delamination in stainless steel/carbon steel composites with laser ultrasonic

This paper proposed the non-contact non-destructive detection of internal delamination defects in stainless steel/carbon steel laminated strip with laser ultrasonic detection method. The finite element model of stainless steel/carbon steel laminated plates was built to analyze the propagation of laser ultrasound in stainless steel/carbon steel laminated plates. The influences of delamination defects on laser ultrasonic wave field distribution and delamination size on the amplitude of laser ultrasound waves were analyzed to investigate the interaction between laser ultrasound and delamination defects. The out-of-plane displacements which were very sensitive to the delamination defects of laser ultrasonic waves were used to characterize delamination defects. The experimental signals were in good agreement with simulation waveforms. The laser ultrasonic method based on waveform amplitude is effective for non-contact detection of delamination in stainless steel/carbon steel laminated strip, which can provide data support for the optimization of rolling process.

A comparison of machine learning algorithms for assessment of delamination in fiber-reinforced polymer composite beams

Structural health monitoring techniques based on vibration parameters have been used to assess the internal delamination damage of fiber-reinforced polymer composites. Recently, machine learning algorithms have been adopted to solve the inverse problem of predicting delamination parameters of the delamination from natural frequency shifts. In this article, a delamination detection methodology is proposed based on the changes in multiple modes of frequencies to assess the interface, location, and size of delamination in fiber-reinforced polymer composites. Three types of machine learning algorithms including back propagation neural network, extreme learning machine, and support vector machine algorithm were adopted as inverse algorithms for assessment of the delamination parameters, with a special focus on the interface prediction. A theoretical model of fiber-reinforced polymer beam with delamination under vibration was constructed to learn how the frequencies are affected by the delaminations (“forward problem”) and to generate a database of “frequency shifts versus delamination parameters” to be used in machine learning algorithms for delamination prediction (“inverse problem”). Multiple carbon/epoxy fiber-reinforced polymer beam specimens were manufactured and measured by a laser scanning Doppler vibrometer to extract the modal frequencies. Numerical and experimental verification results have shown that support vector machine has the best prediction performance among the three machine learning algorithms, with high prediction accuracy and only requiring a small number of samples. For predicting the interface of delamination which is a discrete variable, support vector machine classification has observed better prediction accuracy and requiring less running time than regression. This study is one of the first to prove the applicability of support vector machine for structural health monitoring of delamination damage in fiber-reinforced polymer composites and has the potential to improve the prediction capability of machine learning algorithms. Another significant outcome of the study is that the interface of delamination has been predicted accurately with support vector machine.

Vibrational detection of delamination in composites using a combined finite element analysis and machine learning approach

One common challenge of using composite materials is detecting delamination failure in a non-destructive and cost-effective way. Past studies have proven the feasibility of using vibrational measurements to detect damage but have not explored the full capabilities and limitations of vibrational testing. Here, we use a finite element model of a composite plate to characterize the natural frequency, mode shape, and mode curvature tests for a variety of delamination scenarios. We find that the mode curvature test is resource-intensive to conduct but provides the best resolution in both identifying and localizing delamination. On the other hand, the natural frequency test is simple and inexpensive to conduct but can only reliably identify the presence of delamination. Additionally, a machine learning model is implemented to augment the natural frequency test, allowing both localization of damage and quantification of its severity with only the natural frequencies of modes 1–6. We are able to interpret our model and discover a phase transition for natural frequencies with different sized delaminations. This testing framework allows rapid non-destructive analysis for the iterative design of composites, accelerating the development of novel delamination-resistant materials.

Wavelet-aided guided wavefield imaging of delaminations in laminated composite plates

Delamination is a commonly encountered type of damage in composite structures, which may lead to degraded performance and even failure of structures. Thus, detection of delaminations is considered a vital task to maintain structural integrity and reliability. This paper presents a delamination imaging method based on the analysis of guided wavefields. The method relies on the fact that delaminations in a structure will cause wavefield anomalies, which in turn can be utilized for the indication of delamination zones. Such anomalies, however, could be easily masked by the major components of the wavefield signals including incident waves from actuators and reflected waves from structural boundaries. To address the problem, one-dimensional wavelet transform (WT) is used to process the wavefields leading to WT wavefields. The WT wavefields can highlight signal anomalies associated with delaminations. A WT wavefield imaging algorithm is first constructed step by step in detecting multiple delaminations in a glass fiber reinforced polymer (GFRP) plate. The algorithm is then successfully used in the imaging of a single delamination zone in a carbon fiber reinforced polymer (CFRP) plate. The locations, sizes, and shapes of the delamination zones in the GFRP and CFRP plates are clearly visualized in the ultimate damage maps.