Damage Detection In Composites Research Articles

Microwave time reversal for nondestructive testing of buried small damage in composite materials

Composite materials are widely applied in aerospace, civil engineering, and sports equipment. Various damages produced during fabrication and long-term use can destroy its original mechanical properties, which brings safety and structural healthy concerns. Microwave imaging based on time reversal (TR) is one of the most promising nondestructive testing methods for portable, low-cost, and accurate testing with the advantages of auto-focus and super-resolution. This paper applied microwave TR for the detection of buried small damage in composites backed by metal plates. Strong reflection from composite–metal interfaces brings challenges in successfully achieving time-reversal auto-focusing on small and weak-scattering damages in composites. Traditional target localization methods, including the entropy regularization method and time-integrated energy method, may result in the wrong localization of small damages. The main contribution of this paper is that the localization problem caused by the strong reflection from metal plates is revealed first, and the target initial reflection method from through-wall-radar imaging is introduced to solve it. The performance of three target localization methods is investigated, and the physical reasons for failure or successful localization are discussed in detail. Some performance influence factors, such as the arrangement of receivers or the total time step of received signals, are also discussed. Good performance for the detection of a single small damage with a weak scattered signal is achieved, and the performance for detecting multiple damages is studied. All time-reversal simulations are carried out based on the finite-difference time-domain method.

Research on low‐velocity impact resistance of carbon fiber composite laminates

AbstractThis research conducts a comprehensive experimental and numerical analysis of carbon fiber composite laminates under low‐velocity impact conditions. Split Hopkinson Pressure Bar (SHPB) was employed to conduct impact testing on laminates with two layup sequences ( and ) under impacts ranging from 2 to 6 J. For assessing damage in laminates, a comprehensive progressive damage model and a simplified model were developed. The primary distinction between these models lies in the fact that the former incorporates the strengthening effect of compression on the delamination behavior, while the latter does not. Both models produce a good agreement with experimental results in terms of force–time, force–displacement curves, and absorbed energies. However, notable differences were noted in the predicted internal damage; the simplified model was underpredicted in terms of damage size and distribution compared to the comprehensive progressive damage model.Highlights Impact tests were conducted on and composites. In order to simulate damage caused by impacts in laminates, an energy‐based damage model was created, and cohesive elements were utilized to model interlaminar delamination. The strengthening effect of compression was explored. Distinct damage criteria were established for two cases. Damage detection of composites by x‐rays.

Damage detection in composites using non-destructive testing aided by ANN technique: A review

Damages are inevitable in structures and effective damage detection techniques are important for maintaining their health. Many weight-sensitive engineering applications use composite materials, especially fiber-reinforced laminates. Common damages of these materials include delamination, fiber breakage, fiber pull-out, etc. Various non-destructive testing (NDT) techniques are reported in the literature for damage detection in composites, such as ultrasonic testing, vibration-based techniques, acoustic emission technique, optical NDT and imagining techniques. However, due to the complex properties of composite materials, conventional techniques for analyzing NDT data are difficult to implement. In this context, artificial neural network (ANN) technique is a promising alternative for analyzing NDT data for damage detection. In this study, an attempt is made to explore the state-of-the-art of damage detection in composites using NDT aided by ANN. The work discusses the pros and cons of different methods and is expected to help in identifying the appropriate method for damage detection in composites.

Deep transfer learning-based damage detection of composite structures by fusing monitoring data with physical mechanism

Damage detection of carbon fiber reinforced plastics (CFRP) composites is a challenging issue owing to their intrinsic high degree of anisotropy with complex failure modes. Data-driven approaches have great potential in damage detection for CFRP composites. However, the lack of physical interpretability makes data-driven methods highly dependent on the amount of data, which involves significant effort in experiment and is impractical to obtain for all damage conditions. To overcome the above challenges, a robust and generalizable framework for damage detection of CFRP composite structures is proposed by fusing monitoring data with physical mechanisms. In this framework, the numerical method is leveraged to build physical models of the composite structures to generate data under various damage conditions. Then, a deep transfer learning-based model is applied in the fusion of experiment and simulation data, which mitigates the discrepancy between the physical model and real experiment. With the combination of domain adaptation and domain adversarial training in the model, the domain invariant features can be generalized from the source domain (simulation data) to the target domain (experiment data). Therefore, the physical interpretability is supplemented in the data-driven model. To verify the adaptability of the method, different transfer tasks based on the accelerated aging experiments are performed. The results show that the proposed method can reduce the dependence of the data-driven methods on real monitoring data and prevails in all evaluation indicators than other methods. Eventually, this method can reduce the experiment effort of damage detection for composites while holding a great detection accuracy.

Qualification of distributed optical fiber sensors using probability of detection curves for delamination in composite laminates

Despite the promising application of Distributed Optical Fiber Sensors (DOFS) in monitoring damage in composite structures, their implementation outside academia is still unsatisfactory due to the lack of a systematic approach to assessing their damage detection performance. The existing tool developed for non-destructive evaluation, Probability of Detection (POD) curves, needs to be adapted for structural health monitoring applications to account for spatial and temporal dependency. Damage detection performance with DOFS is deeply related to the inherent variability sources of the system, the strain transfer properties of the optical fiber, and the loading conditions, which determine the damage-induced strain on the structure. This paper establishes a systematic approach based on the Length at Detection (LaD) method to qualify DOFS for damage detection in composites under different scenarios. Specifically, this study considers two DOFS with different strain transfer properties for monitoring delamination in carbon fiber reinforced polymers double-cantilever beam specimens under mode I quasi-static and fatigue loading. The POD curves derived from the LaD method confirm that this methodology can quantify the change in the detection performance due to the DOFS type and the loading conditions. The study also proposes a practical solution to compare POD curves obtained with different sample sizes, by introducing the concept of virtual specimens to simulate the lower confidence bound convergence.

Influence of the geometric parameters on the effective properties of piezoelectric composite sensors using real measurements and a new RVE

Smart structures have attracted the attention of many researchers due to their applications and potential, such as structural integrity monitoring, vibration and noise suppression, shape control, and damage detection in composites. The development of these structures is closely linked to advances in piezoelectric sensors and actuators. One of the most used options in industries is the Macro Fibre Composites (MFC), which is manufactured by using different components, such as Kapton, Copper Electrode, Piezoceramic Fibre and Epoxy Composite, Copper Electrode, Kapton. Two main types of MFC available are d33 and d31, and they play a fundamental role in the advancement of smart structure technology. This work carried out numerical analyses to evaluate the influence of each component on the effective coefficients of the constitutive matrix for piezoelectric composite d33, based on experimentally measured geometric parameters. The investigated parameters were thickness of the protective layers, width, and thickness of the electrodes. Finite Element Method (FEM) combined with the concepts of a proposed Representative Volume Element (RVE) and homogenization of a periodic medium were used to determine the effective coefficients of the transducer. Through the results, it was possible to identify better geometric specifications for the transducer components in order to obtain higher mechanical and electrical properties.

Fatigue Damage Diagnostics of Composites Using Data Fusion and Data Augmentation With Deep Neural Networks

AbstractComposite materials can be modified according to the requirements of applications, and hence, their applications are increasing significantly with time. Due to the complex nature of the aging of composites, it is equally challenging to establish structural health monitoring techniques. One of the most applied non-destructive techniques for this class of materials is using Lamb waves to quantify the damage. Another important advancement in damage detection is the application of deep neural networks. The data-driven methods have proven to be most efficient for damage detection in composites. For both of these advanced methods, the burning question always has been the requirement of data and quality of data. In this paper, these measurements were used to create a framework based on a deep neural network for efficient fault diagnostics. The research question developed for this paper was as follows: Can data fusion techniques used along with data augmentation improve the damage diagnostics using the convolutional neural network? The specific aims developed to answer this research question were: (1) highlighting the importance of data fusion methods, (2) underlining the importance of data augmentation techniques, (3) generalization abilities of the proposed framework, and (4) sensitivity of the size of the dataset. The results obtained through the analysis concluded that the artificial intelligence techniques along with the Lamb wave measurements can efficiently improve the fault diagnostics of complex materials such as composites.

Broadband nonlinear elastic wave modulation spectroscopy for damage detection in composites

A non-destructive testing procedure is proposed for damage detection in composites using full wavefield measurement obtained using a scanning laser Doppler vibrometer. Vibrations are excited using two low-power piezoelectric actuators leading to nonlinear elastic wave modulation at the defect. One actuator is supplied with a broadband chirp signal and the other actuator is supplied with a single-frequency sine signal. First, a time–frequency filtering method is proposed to extract specific nonlinear components of interest (e.g. second higher harmonic and first modulation sideband) without the need for multiple excitation sequences. Next, damage maps are constructed using broadband bandpower calculation of the filtered nonlinear components. It is demonstrated that the modulation sidebands provide an exclusive imaging of defect nonlinearity, and are not affected by potential source nonlinearity. The proposed damage map construction procedure is applied for various carbon fiber reinforced polymer test specimens with different damage features: (1) coupon with quasi-static indentation damage, (2) coupon with artificial delaminations, (3) bicycle frame with impact damage, and (4) stiffened aircraft panel with partially debonded stiffener. The obtained results indicate the high performance of the developed procedure for detection of various defect types in curved and/or stiffened carbon fiber reinforced polymer components.

Piezoelectric PAN/BaTiO3 nanofiber membranes sensor for structural health monitoring of real-time damage detection in composite

Structural health monitoring (SHM) plays a significant role to avoid the catastrophic failure of composites during practical applications. This research developed a polyacrylonitrile and flexible barium titanate (PAN/BaTiO3) piezoelectric nanofiber membranes sensor for real-time damage detection in composites. The conversion between mechanical energy and electrical energy of the sensor was verified by an electrochemical system. The introduction of BaTiO3 nanoparticles improved the mechanical-to-electrical output of 9.3 V of peak values with 15 wt% loaded piezoelectric nanofiber membranes. In the application test, slight deformation on the piezoelectric nanofiber membranes sensor instantly lighted up the commercial LED bulbs. Moreover, the piezoelectric PAN/BaTiO3 sensor still created piezoelectric effect within the composite after curing with carbon fiber, and improved the interlaminated shear strength. The bending and piezoelectric properties of the composite were tested simultaneously. The composite showed good response to electrical signals during the mechanical loading and achieving real-time monitoring of the delamination failure. This sensor showed good potential as a flexible reinforcement in composite materials for SHM applications.

Cumulative acoustic emission energy for damage detection in composites reinforced by carbon fibers within low-cycle fatigue regime at various displacement amplitudes and rates

In the present article, acoustic emission signals were utilized to predict the damage in polymer matrix composites, reinforced by carbon fibers, in the low-cycle fatigue regime. Displacement-controlled fatigue tests were performed on open-hole samples, under different conditions, at various displacement amplitudes of 5.5, 6.0, 6.5 and 7.0 mm and also under various displacement rates of 25, 50, 100 and 200 mm/min. After acquiring acoustic emission signals during cycles, two characteristic parameters were used, including the energy and the cumulative energy. Obtained results implied that the energy parameter of acoustic emission signals could be used only for the macroscopic damage, occurring at more than 65% of normalized fatigue cycles under different test conditions. However, the cumulative energy could properly predict both microscopic and macroscopic defects, at least two failure types, including matrix cracking at first cycles and the fiber breakage at last cycles. Besides, scanning electron microscopy images proved initially such claims under all loading conditions.

Improving the EMI-based damage detection in composites by calibration of AD5933 chip

In this paper results of calibration of Analog Devices AD5933 chip for damage detection problems based on electromechanical impedance (EMI) are presented. A simple calibration method using mid-frequency point and resistor with known resistance was utilised. Authors investigated the influence of calibration process on the measurement results and its agreement with professional measurement equipment. Results obtained from AD5933 were compared with results obtained by professional impedance analyser HIOKI IM3570. Moreover, the influence of lack and wrong calibration of the AD5933 chip is presented. Calibration was validated for the case of damage detection in the carbon fibre reinforced polymer CFRP panel with delamination. Damage indices in the form of RMSD and CCD were utilised. Influence of changing temperature was also included and compensated. Good agreement of results from AD5933 and HIOKI IM3570 was achieved in the case of EMI spectra as well as calculated damage indices. The EMI spectra of magnitude of impedance, phase angle, resistance, conductance, reactance and susceptance were investigated. The obtained results pave the way for effective employment of the AD5933 chip for damage assessment in composite structures.

Damage Detection in Composites By Artificial Neural Networks Trained By Using in Situ Distributed Strains

In this paper, a passive structural health monitoring (SHM) method capable of detecting the presence of damage in carbon fibre/epoxy composite plates is developed. The method requires the measurement of strains from the considered structure, which are used to set up, train, and test artificial neural networks (ANNs). At the end of the training phase, the networks find correlations between the given strains, which represent the ‘fingerprint’ of the structure under investigation. Changes in the distribution of these strains is captured by assessing differences in the previously identified strain correlations. If any cause generates damage that alters the strain distribution, this is considered as a reason for further detailed structural inspection. The novelty of the strain algorithm comes from its independence from both the choice of material and the loading condition. It does not require the prior knowledge of material properties based on stress-strain relationships and, as the strain correlations represent the structure and its mechanical behaviour, they are valid for the full range of operating loads. An implementation of such approach is herein presented based on the usage of a distributed optical fibre sensor that allows to obtain strain measurement with an incredibly high resolution.

A printed electrical impedance tomography sensor for detection of damage in composites

This paper proposes a new printed electrical impedance tomography (EIT) sensor for detection of damage in composite structure. The EIT sensor consists of a sensing area and related electrical circuits. It is directly printed on the surface of composite structure by a layer-by-layer screen printing process with graphene-doped carbon ink and silver ink. By injecting tiny currents into the EIT sensor and measuring the corresponding boundary voltages, damage information can be obtained by inverse analysis to reconstruct the distribution of conductivity change caused by damage. It has the potential to quantitatively identify the locations and sizes of multiple damage without any prior knowledge. Experimental results on a glass fiber reinforced polymer (GFRP) plate with different number of simulated damage have demonstrated the effectiveness of the printed sensor itself and the related damage detection method.

Impact damage detection in composites using a guided wave mixing technique

Combined harmonics at sum- or difference-frequencies can be generated by the cross-interactions of guided wave mixing in tested samples with material nonlinearity. However, it is also observed that there are multiple combined harmonic modes generated at various mixing frequencies during the guided wave mixing process. It is still difficult to select certain combined harmonic modes to identify damage because the acoustic nonlinear response is much more complicated than the damage itself. Considering that multiple mixing frequency components can be generated during guided wave mixing, this paper investigates the feasibility of detecting impact damage in composite laminates through the combination of a guided wave mixing technique and a mixing frequency peak counting approach. Experimental observation of the combined harmonics at the mixing frequency generated by guided wave mixing clearly identifies the existence of material nonlinearity in the specimen. The present results show a monotonic increase in the correlation of the value of the mixing frequency spectral peak count with respect to the impact energy induced in the specimens. The developed technique provides an alternative for practical application of guided wave mixing for nondestructive testing in a quantizable manner.

Real-time strain monitoring and damage detection of composites in different directions of the applied load using a microscale flexible Nylon/Ag strain sensor

Composites are prone to failure during operating conditions and that is why vast research studies have been carried out to develop in situ sensors and monitoring systems to avoid their catastrophic failure and repairing cost. The aim of this research article was to develop a flexible strain sensor wire for real-time monitoring and damage detection in the composites when subjected to operational loads. This flexible strain sensor wire was developed by depositing conductive silver (Ag) nanoparticles on the surface of nylon (Ny) yarn by electroless plating process to achieve smallest uniform coating film without jeopardizing the integrity of each material. The sensitivity of this Nylon/Ag strain sensor wire was calculated experimentally, and gauge factor was found to be in the range of 21–25. Then, the Nylon/Ag strain sensor wire was inserted into each composite specimen at different positions intentionally during fabrication depending upon the type of damage to detect. The specimens were subjected to tensile loading at a strain rate of 2 mm/min. Overall mechanical response of composite specimens and electrical response signal of the Nylon/Ag strain sensor wire showed good reproducibility in results; however, the Nylon/Ag sensor showed a specific change in resistance in each direction because of the respective position. The strain sensor wire designed not only monitored the change in the mechanical behavior of the specimen during the elongation and detected the strain deformation but also identified the type of damage, whether it was compressive or tensile. This sensor wire showed good potential as a flexible reinforcement in composite materials for in situ structural health monitoring applications and detection of damage initiation before it can become fatal.

Magnetostrictive Sensors for Composite Damage Detection and Wireless Structural Health Monitoring

The efficacy of magnetostrictive ribbon actuators as aerospace composites impact damage detectors has been investigated through finite-element modeling and experimental studies, investigating both the sensitivity of magnetostrictive ribbons embedded and surface mounted using the tensile and three-point bending tests. From the modeling, it was found that the surface-mounted ribbons increased Young’s modulus of the system compared to the composite alone but caused the ribbons to delaminate from the surface before failure. The embedded ribbons did not appear to affect the structural properties of the composite, which was observed through the three-point bending tests carried out. From the impact damage tests, it was determined that the ribbons had to be embedded two-ply below the surface to measure impact energies greater than 1.6 J. For surface-mounted ribbons, damages of 1.6 J to the surface could be detected and pinpointed for two ribbons 10 mm apart. We also demonstrate in a simple way how a two-ribbon scheme may be used to determine the damage position in the tested sample, which may be extended for wireless sensing.

Detection of composite damage by IR NDT using ultrasonic and optic excitation

The submitted paper presents experience with detection of damaged composites by means of an ultrasonic excitation system in comparison with an optic system of excitation. In case of the ultrasonic excitation system is a high-frequency signal modulated by lock-in frequency. For a selection of the carrier signal we measured a response of a sample to sweep excitation and a sample resonance was defined by means of STFT. Consequently, the method lock-in was applied for detection. In case of the optic excitation system we applied several suitably selected frequencies with an aim to obtain information from various depths of the measured object. Results of measurements are presented in composite samples of CFRP.

Early Damage Detection in Composites during Fabrication and Mechanical Testing.

Fully integrated monitoring systems have shown promise in improving confidence in composite materials while reducing lifecycle costs. A distributed optical fibre sensor is embedded in a fibre reinforced composite laminate, to give three sensing regions at different levels through-the-thickness of the plate. This study follows the resin infusion process during fabrication of the composite, monitoring the development of strain in-situ and in real time, and to gain better understanding of the resin rheology during curing. Piezoelectric wafer active sensors and electrical strain gauges are bonded to the plate after fabrication. This is followed by progressive loading/unloading cycles of mechanical four point bending. The strain values obtained from the optical fibre are in good agreement with strain data collected by surface mounted strain gauges, while the sensing regions clearly indicate the development of compressive, neutral, and tensile strain. Acoustic emission event detection suggests the formation of matrix (resin) cracks, with measured damage event amplitudes in agreement with values reported in published literature on the subject. The Felicity ratio for each subsequent loading cycle is calculated to track the progression of damage in the material. The methodology developed here can be used to follow the full life cycle of a composite structure, from manufacture to end-of-life.

Sensitivity analysis of inverse algorithms for damage detection in composites

Vibration-based damage detection is a promising structural health monitoring technique for identifying delaminations in composite structures. However, its accuracy may be adversely affected by measurement errors and the discrepancy between the real composite structures and their numerical models. Therefore, it is of critical importance to study the effect of such noise on the prediction accuracy. The ideal way to do this is to analyze the noise effects based on real measurements on large number of specimens. However, evidently, this is cost and time intensive in terms of materials, manufacturing and labor. An alternative way is to add artificial noise to the structural dynamic parameters obtained from numerical model and study diverse cases using this modified numerical data. In this paper, different levels of random artificial noise (1%, 2%, 3% and 5%) are added to the numerical frequencies. In order to prevent any undesirable bias, 10 different sets of random noise, both uniformly and normally distributed, are employed at each noise level for the same frequencies. The frequency shifts from these 10 test cases (at each noise level) were then input into three inverse algorithms, namely graphical technique (GT), surrogate-assisted optimization (SAO) and artificial neural network (ANN), to predict the location, size and interface of delaminations in a composite beam. The predictions were subsequently compared to the actual damage parameters to evaluate the effect of the noise on the prediction accuracies. SAO and ANN were also tested with 100 cases of noise and then further with Monte Carlo simulation with 100,000 samples in ANN to verify the results. Additional noise sensitivity tests were carried out on beams with different delamination sizes and interfaces, to investigate the dependence of noise effect on the damage parameters themselves. Results show that the trends in prediction errors with increasing noise levels become more consistent as the number of samples increased. With 100,000 samples, the predictions by ANN with uniformly distributed noise and Gaussian noise were nearly indistinguishable. Furthermore, prediction accuracies are also affected by the location and size of the delamination. Among the three inverse algorithms, the performance of ANN was found to be less reliable in presence of noise compared to SAO and GT.

Impact damage detection in smart composites using nonlinear acoustics—cointegration analysis for removal of undesired load effect

The paper presents a reliable methodology—based on nonlinear acoustics—for impact damage detection in composite materials. The nonlinear vibro-acoustic wave modulation technique is used to detect damage. The problem of operational variability of the method with respect to the selection of frequency and amplitude of low-frequency (LF) modal excitation is investigated. This problem is addressed using the concept of stationarity of time series of vibro-acoustic data. Cointegration analysis is employed to compensate for the effect of variable operational conditions associated with LF modal (or vibration) excitation in nonlinear vibro-acoustic wave modulations. Analysis of stationary statistical characteristics of vibro-acoustic responses—after cointegration analysis—are used for damage detection. The proposed method is validated using vibro-acoustic data from laminated composite plates and composite sandwich panels. The results demonstrate that the proposed approach can effectively compensate for the effect of LF modal excitation on nonlinear vibro-acoustic wave modulations and detect the damage more accurately and robustly than the existing nonlinear acoustics based on the analysis of modulation sidebands.