Health Monitoring Of Composite Structures Research Articles

Classification of damage modes in scaled open-hole composite laminates under combined tension-shear loading using acoustic emission technique

The damage initiation and evolution of carbon fiber-reinforced polymer (CFRP) composite under multi-axial loading is of fundamental interest. This study investigates intra- and interlaminar damage modes induced in open-hole quasi-isotropic (QI) carbon/epoxy composite laminate under combined tension-shear loading using the acoustic emission (AE) technique. The effect of layer thickness is studied using three types of laminates with stacking sequences PL-1: [−452/452/902/02]s, PL-2: [−453/453/903/03]s (ply blocked), and SL-1: [−45/45/90/0]2s (a sub-laminate blocked or distributed). Clustering of acquired AE signals is carried out using hierarchical methods. Damage modes evolution for the open hole QI CFRP is captured using the clustered AE signal data. It is observed that the thickness scaling significantly affects the open-hole strength and the critical failure mechanisms. Dominant damage mechanisms are identified based on AE cumulative energy. Ply-level blocked (PL-1 and PL-2) laminates exhibit lower strength than the SL-1 blocked laminate. The findings from this experimental investigation indicate that the AE technique in conjunction with the clustering approach is a prospective tool for the structural health monitoring of composite structures under combined loading.

Development of AgNPs-PVP/TPU based flexible strain sensors for structural health monitoring of composite structures

Real-time in-stu structural health monitoring (SHM) of composite structure developed through highly sensitive and flexible smart sensors based on colloidal solution of Silver Nano Particles (AgNPs). Synthesis of AgNPs through chemical reduction method by citrate and then in Haxen phase transferred by using surfactant Cetyltrimethylammonium bromide (CTAB). Colloidal solution of AgNPs-Haxen and Polyvinylpyrrolidone (PVP) - Haxen has been followed by mixed, calcined and deposited that thick paste on flexible Thermoplastic Polyurethane (TPU) film. These sensors become more sensitive than metallic foil made sensors and farther this sensor pasted on polymer composite for monotonic loading like tensile loading. The data extracted from sensors was monitored effectively by data acquisitioning technique using Keithley KUSB-3100. This research based on chemical synthesis of metallic nanoparticles like silver, its functionalization, protection of these nanoparticles from environment by capping agents and by creating inert atmosphere respectively. Then these functionalized capped nanoparticles are embedded on the surface of thermoplastic Urethane (TPU) film to make piezo resistive semi conductive strips which serve as flexible strain gauge/smart sensor. One other technique is also being used to make sensor is that colloidal solution of metallic Nanoparticles sprays over TPU substrate using Connective Self Assembly (CSA) method. The sensor then properly attached to Glass fabric reinforced polymer (GFRP) composite and calibrated using data acquisitioning (DAQ) under different mode of testing.

A high-performance, sensitive, low-cost LIG/PDMS strain sensor for impact damage monitoring and localization in composite structures

Health monitoring of composite structures in aircraft is critical, as these structures are commonly utilized in weight-sensitive areas and innovative designs that directly impact flight safety and reliability. Traditional monitoring methods have limitations in monitoring area, strain limit, and signal processing. In this paper, a multifunctional sensor has been developed using acid-treated laser-induced graphene (A-LIG) with a multi-layer three-dimensional conductive network. Compared to untreated laser-induced graphene, the sensitivity of A-LIG sensor is increased by 100%. Furthermore, PDMS is used to fill the pores, which improves the fatigue performance of the A-LIG sensor. To obtain clear monitoring results, a data conversion algorithm is provided to convert the electrical signal obtained by the sensor into a strain field contour cloud map. The impact test of the A-LIG/PDMS sensor on the carbon fiber panel of the aircraft wing box segment verifies the effectiveness of its strain sensing. This work introduces a novel approach to fabricating flexible sensors with improved sensitivity, extended strain range, and cost-effectiveness. The sensor exhibits high sensitivity (gauge factor, GF ≈ 387), is low hysteresis (∼53 ms), and has a wide working range (up to 47%), and a highly stable and reproducible response over multiple test cycles (>18 000) with good switching response. It presents a promising and innovative direction for utilizing flexible sensors in the field of aircraft structural health monitoring.

Novel Response Surface Technique for Composite Structure Localization Using Variable Acoustic Emission Velocity.

The time difference of arrival (TDOA) method has traditionally proven effective for locating acoustic emission (AE) sources and detecting structural defects. Nevertheless, its applicability is constrained when applied to anisotropic materials, particularly in the context of fiber-reinforced composite structures. In response, this paper introduces a novel COmposite LOcalization using Response Surface (COLORS) algorithm based on a two-step approach for precise AE source localization suitable for laminated composite structures. Leveraging a response surface developed from critical parameters, including AE velocity profiles, attenuation rates, distances, and orientations, the proposed method offers precise AE source predictions. The incorporation of updated velocity data into the algorithm yields superior localization accuracy compared to the conventional TDOA approach relying on the theoretical AE propagation velocity. The mean absolute error (MAE) for COLORS and TDOA were found to be 6.97 mm and 8.69 mm, respectively. Similarly, the root mean square error (RMSE) for COLORS and TODA methods were found to be 9.24 mm and 12.06 mm, respectively, indicating better performance of the COLORS algorithm in the context of source location accuracy. The finding underscores the significance of AE signal attenuation in minimizing AE wave velocity discrepancies and enhancing AE localization precision. The outcome of this investigation represents a substantial advancement in AE localization within laminated composite structures, holding potential implications for improved damage detection and structural health monitoring of composite structures.

Enhanced Performance of Morphing Wing Through Composite Fabrication and Structural Health Monitoring

Enhanced Performance of Morphing Wing Through Composite Fabrication and Structural Health Monitoring

Bayesian data-driven framework for structural health monitoring of composite structures under limited experimental data

Ultrasonic-guided waves can be used to monitor the health of thin-walled structures. However, the run of experimental damage tests on materials like carbon fiber-reinforced plastics can be impractical and costly. Instead, numerical models can be used to create hybrid datasets to train machine learning algorithms, integrating data from numerical and experimental tests. This paper presents a Bayesian-driven framework to compensate for limited experimental data regarding Lamb wave propagation in composite plates. Using Bayesian inference, the framework updates a numerical finite element model, considering observed uncertainties by sampling posterior probability density functions for input parameters using Markov–Chain Monte Carlo simulations with the Metropolis-Hastings algorithm. A neural network surrogate model speeds-up these simulations, leading to a model that replicates the uncertain experimental setup. This model then generates data to augment true experimental data. Finally, a one-dimensional convolutional neural network is trained on a three different datasets to analyze Lamb wave signals and assess damage. Comparing training strategies shows the hybrid dataset augmented by samples generated by the updated FE model gives the most accurate damage size predictions.

Online structural health monitoring of polymer composite structure using gold nanoparticles (AuNPs)

A mechanism for online structural health monitoring of composite structure was established by developing highly sensitive strain gauges based on colloidal Gold Nano Particles (AuNPs). They were synthesized using well known Turkevich-Frens method followed by phase transfer in chloroform using surfactant Cetyltrimethylammonium bromide (CTAB). Flexible Strain gauges / smart sensors were formed by mixing colloidal AuNPs-chloroform solution in polystyrene (PS) – chloroform solution and depositing their thick paste on hand-made flexible thermoplastic polyurethane (TPU) film. These sensors were used as strain gauges on composite substrates for tensile testing. The developed AuNP based strain gauges were found to have 10 times higher sensitivity than normal metal foil strain gauges.

Monitoring of Composite Structures for Re-Usable Space Applications Using FBGs: The Influence of Low Earth Orbit Conditions.

Fiber Bragg grating sensors (FBGs) are promising for structural health monitoring (SHM) of composite structures in space owing to their lightweight nature, resilience to harsh environments, and immunity to electromagnetic interference. In this paper, we investigated the influence of low Earth orbit (LEO) conditions on the integrity of composite structures with embedded optical fiber sensors, specifically FBGs. The LEO conditions were simulated by subjecting carbon fiber-reinforced polymer (CFRP) coupons to 10 cycles of thermal conditioning in a vacuum (TVac). Coupons with embedded optical fibers (OFs) or capillaries were compared with reference coupons without embedded OFs or capillaries. Embedded capillaries were necessary to create in situ temperature sensors. Tensile and compression tests were performed on these coupons, and the interlaminar shear strength was determined to assess the influence of TVac conditioning on the integrity of the composite. Additionally, a visual inspection of the cross-sections was conducted. The impact on the proper functioning of the embedded FBGs was tested by comparing the reflection spectra before and after TVac conditioning and by performing tensile tests in which the strain measured using the embedded FBGs was compared with the output of reference strain sensors applied after TVac conditioning. The measured strain of the embedded FBGs showed excellent agreement with the reference sensors, and the reflection spectra did not exhibit any significant degradation. The results of the mechanical testing and visual inspection revealed no degradation of the structural integrity when comparing TVac-conditioned coupons with non-TVac-conditioned coupons of the same type. Consequently, it was concluded that TVac conditioning does not influence the functionality of the embedded FBGs or the structural integrity of the composite itself. Although in this paper FBG sensors were tested, the results can be extrapolated to other sensing techniques based on optical fibers.

Impact force localization and reconstruction via gated temporal convolutional network

Techniques for identifying impact forces are crucial for improving the operational reliability and safety of aerospace composite structures. To address the challenges of theoretical modeling that arise from the complexity of composite structures, we propose a Gated Temporal Convolutional Network (GTCN) method. This method establishes inverse mapping relationships between structural vibration responses and impact forces. The GTCN increases the receptive field through dilated convolution, concurrently employing gating mechanisms to extract pivotal features from input signals in the vertical direction. Simulations and experiments are conducted to robustly validate the efficacy and applicability of the proposed impact force identification method. The simulation results demonstrate that the GTCN method is capable of obtaining robust impact force localization and reconstruction results, even amidst conditions of elevated noise levels. The average localization error of the GTCN is kept within 20 mm even at a noise level of 5 dB. Meanwhile, the GTCN method achieves satisfactory identification results for different types of impact forces (Gaussian, semi-cosine, and sawtooth), with an average localization accuracy above 95%. The applicability of the GTCN method to real-world structure is verified in the experiments. The average relative error of the reconstructed forces by GTCN is less than 15%. GTCN achieves stable identification of impact forces when the number of sensors is larger than 2. In both simulations and experiments, the GTCN performs better than Temporal Convolutional Network (TCN) and Convolutional Neural Network (CNN) with higher identification accuracy. The proposed method demonstrates considerable potential for application in the health monitoring of aircraft composite structures.

Ring Array Scatter Feature Adaption Deep Transfer Imaging Method for Composite Plate Structure Health Monitoring Using Guided Waves

Ring Array Scatter Feature Adaption Deep Transfer Imaging Method for Composite Plate Structure Health Monitoring Using Guided Waves

Strain Measurement with Optic Fibers for Structural Health Monitoring of Woven Composites: Comparison with Strain Gauges and Digital Image Correlation Measurements.

In this work, the strains measured with optic fibers and recorded during tensile tests performed on carbon/epoxy composite specimens were compared to those recorded by strain gauges and by Digital Image Correlation (DIC). The work aims at investigating the sensitivity of embedded and glued optic sensors for structural health monitoring applications in comparison with strain gauges and the full field strain map of the DIC. Acrylate, polyimide optic fibers, and three strain gauge sizes are considered to compare the three techniques. Results show hard polyimide-coated sensors are more sensitive to the material pattern than soft acrylate-coated fibers, which also require extensive adhesion length. The work shows a comparable size of strain gauges and material meso-structure is also critical for properly assessing material properties. The Young's modulus computed with the three different techniques is used to define a strategy that supports the selection and the proper size of the adopted strain measuring system for structural health monitoring of composite materials.

Validation of large area capacitive sensors for impact damage assessment

Impacts in fiber-reinforced polymer matrix composites can severely inhibit their functionality and prematurely lead to the composite’s failure. This research focuses on determining the efficacy of a novel capacitive sensor, termed as the soft elastomeric capacitor (SEC), to monitor the magnitude of out-of-plane deformations in composites. This work forwards the development of a sensing skin that can be used as an in situ monitoring tool for composites. The capacitive sensor can be made to arbitrary sizes and geometries. The sensor is composed of an elastomer composite that measures strains experienced by the material it is bonded to. The structure of the sensor, fabricated to function as a parallel plate capacitor, responds to impacts by transducing strains into a measurable change in capacitance. In this work, the SECs are deployed on randomly oriented fiberglass-reinforced plates with a polyester resin matrix. The material is impacted at various energy levels until the monitored composite material reaches its yielding point. The behavior of the sensor in impact detection applications below the proof resilience shows little to no change in the capacitance of the sensor. As the impacts surpass this yielding point, the sensor responds linearly with induced change in the area. The sensor performed within the expectations of the proposed model and demonstrated the efficacy of the proposed large-area sensor as a damage quantification tool in the structural health monitoring of composites.

Damage localization in carbon fiber composite plate combining ultrasonic guided wave instantaneous energy characteristics and probabilistic imaging method

The ultrasonic guided wave (UGW) imaging technique has become a promising tool in composite structure health monitoring. However, most algorithms extract damage characteristics with time or frequency domain method. These methods cannot characterize the nonlinear and nonstationary damage signals. Based on the Hilbert–Huang transform, the phenomenon of instantaneous energy increase and decrease is studied, and the rule of energy change and distance from damage to sensor path is summarized. The damage index based on instantaneous energy characteristics is proposed. A probabilistic imaging method is proposed, which introduces a probability distribution function based on the elliptical method for fusion imaging with traditional probability distribution function. The experiment of 74 points on carbon fiber composite plate was performed. The average localization error was 3.56 mm and the maximum was 9.68 mm. The proposed method has high localization accuracy and stability and has application prospects in high-end equipment composite structure health monitoring.

Materials Characterisation and Structural Health Monitoring of Composite Structures Using Ultrasonic Guided Wave

The ultrasonic guided waves are employed to develop a sensor system that can be permanently attached to the aerospace composite components for the purpose of continuous or intermittent monitoring of the health of the structure. The system is capable of detecting impact and dis-bond damages that occur during the service of the aircraft. The sensor patch design, sensor reliability, low footprint sensor electronics, data processing and evaluation automation, wireless communication, energy requirements, etc. will be discussed in this paper.

Embedding Fiber Bragg Grating Sensors in Carbon Composite Structures for Accurate Strain Measurement

Fiber Bragg Grating (FBG) sensors written by femtosecond laser pulses in polyamide-coated low bending loss optical fibers are succesfully embedded in carbon composite structures, following laminating and light resin moulding processes which optimize the size of each ply to address aesthetic, drapability and structural requirements of the final components. The sensors are interrogated by a tunable laser operating at around 1.55 μm and their response to temperature and strain variations is characterized in a thermally controlled chamber and by bending tests using suspended calibrated loads and a laser scanning system. Experimental results are in good agreement with simulations, confirming that the embedding process effectively overcomes potential issues related to FBG spectral distortion, birefringence and losses. In particular the effects of the composite material non homogeneity and FBG birefringence are investigated to evaluate their impact on the monitoring capabilities. A bi-material mechanical beam model is proposed to characterize the orthotropic laminates, pointing out better accuracy in estimating the applied load with respect to the classical homogeneous beam model. A comparative analysis, performed on different instrumented carbon composite samples and supported by theory, points out the repeatability of the FBG sensors embedding process and the effectiveness of the technology for real time accurate strain measurement. Based on such measurements damages and/or changes in local stiffness can be effectively detected, allowing for structural health monitoring of composite structures for applications in specific industrial fields like automotive and aerospace.

The effect of temperature on guided wave signal characteristics in presence of disbond and delamination for health monitoring of a honeycomb composite sandwich structure with built-in PZT network

Guided wave (GW) based structural health monitoring (SHM) techniques being developed by researchers frequently use amplitude and group velocity variations between healthy and damage-affected GW modes to detect and localise damage. Nonetheless, external variables such as temperature and moisture influence these features, which were not considered in previous studies, particularly in the presence of damage in honeycomb composite sandwich structures (HCSSs). Therefore, a coordinated numerical and experimental study was carried out in an effort to examine the characteristics of GW propagation in an HCSS for two damages: a disbond between the face sheet and the core, and delamination between the face sheet layers for a temperature range of 0 ∘C–90 ∘C. Computationally efficient two-dimensional numerical models were developed using COMSOL Multiphysics that takes into account a variety of temperature-related phenomena, such as thermal stresses and changes in the material properties of honeycomb sandwich and piezoelectric wafer transducers (PZTs). The amplitude and group velocity of the fundamental anti-symmetric (A0) mode are found to increase in the presence of a disbond and decrease in the presence of face sheet delamination. However, it is observed that there is a linear decrease in the amplitude of A0 mode for both the healthy and damaged cases with an increase in temperature. Since the A0 mode is widely employed for interrogation due to its defect sensitivity, an amplitude and group velocity adjustment equation with temperature change is proposed. Finally, considering the amplitude difference of normalised A0 mode, the two damages are localised within a network of PZTs by using a probability-based signal difference coefficient method, which is found to be efficient and reliable for SHM of HCSS under variable temperature conditions.

Guided waves propagation in arbitrarily stacked composite laminates: Between-layers incompatibility issue resolution using hybrid matrix strategy

Lamb waves (LWs) are widely used to achieve structural health monitoring of aeronautic composite structures. Composite materials are however anisotropic and LWs propagation characteristics depend on their propagation direction. Within one composite ply, when this direction coincides with the principal axis of the ply, they are not coupled with shear waves (SHWs) but become coupled with SHWs for any other direction. As composite materials are built up with layers at various orientations LWs and SHWs are coupled for some layers and uncoupled for some others when studying an arbitrary propagation direction. Transfer Matrix Method (TMM), Global Matrix Method (GMM), and Stiffness Matrix Method (SMM) are all methods allowing to predict guided waves (LWs and SHWs) behavior in composite materials. However those methods suffer from an incompatibility issue preventing them to manage cases when SHWs and LWs are coupled for some plies but uncoupled for some other plies. This issue is particularly frequent when dealing with metallic-composite plates or with composite plates made up with isotropic, orthotropic, and triclinic materials. In order to solve this incompatibility issue, a hybrid matrix strategy (HMS) is proposed here on the basis of SMM. The core idea of the HMS is to re-couple LWs and SHWs into hybrid guided waves when they are uncoupled in order to make them compatible with the general coupled waves cases. The numerical stability of HMS is proved theoretically and its effectiveness is validated through numerical investigations and using experimental data from the literature. The SMM-HMS framework can thus be considered as a state of the art benchmark approach for evaluating the performance of numerical methods dedicated to the computation of guided waves dispersion curves and can be confidently applied to any arbitrary composite material used in aeronautic and aerospace industries.

Intelligent structural health monitoring of composite structures using machine learning, deep learning, and transfer learning: a review

Structural health monitoring (SHM) methods are essential to guarantee the safety and integrity of composite structures, which are extensively utilized in aerospace, automobile, marine, and infrastructure industry. The deterioration of composite structures is primarily caused by operational and environmental variability. To address this issue, artificial intelligence (AI) techniques are being integrated into the SHM systems to enhance the performance of composite structures via digital transformation and big data analysis. Therefore, the present article aims to provide a critical review of AI models, including machine learning, deep learning, and transfer learning, to preserve and sustain composite structures throughout their life. The article covers the complete SHM process for composite structures, including sensing technologies, data-preprocessing, feature extraction, and decision-making process. Thus, the health monitoring of composites is presented in consideration of modern AI techniques, accompanied by the identification of current challenges and potential future research directions.

In-situ cure monitoring of thick CFRP using multifunctional piezoelectric-fiber hybrid sensor network

Conventional process monitoring techniques rely on point or line measurements and can only monitor very limited physical quantities during the curing process. In this study, an innovative multifunctional piezoelectric-fiber hybrid sensor network is developed for global monitoring of multiple physical quantities during the curing cycle of thick carbon fiber-reinforced polymer (CFRP) composites. Three different approaches based on non-invasive and integrated piezoelectric lead-zirconate-titanate (PZT) sensor network are effectively developed to track the degree of cure and key steps via the velocity and amplitude of the received guided wave. The bare and encapsulated fiber optic sensors are woven into the fabrics along the through-thickness direction before liquid composite molding processing to monitor the key steps and the development of temperature and cure-induced strain gradient. Efforts are also made to perform three-dimensional curing imaging from the measurements of the developed sensor network. Experimental validation demonstrates the effectiveness of the proposed piezoelectric-fiber hybrid sensor network for in-situ and real-time monitoring of multiple physical quantities during cure, contributing to the advancement in smart manufacturing and life-cycle structural health monitoring of composites.

Damage quantification in composite laminates based on sparsity-difference estimation of the normal time–frequency representation

Damage index (DI) is of vital importance for the health monitoring of composite structures. However, most of the existing DI construction methods cannot reveal the rich information hidden in Lamb waves, such as the time-varying characteristics of the signal amplitude and phase, and thus cannot well characterize the evolutionary trend of damage. To address this limitation, this study proposes a novel DI construction method for damage identification in composite structures. The normal time–frequency transform is first applied to obtain the unbiased time–frequency distribution of the received Lamb wave signal, and then the singular value decomposition method is used to obtain the singular value sequence of the two-dimensional time–frequency matrix to characterize its matrix sparsity. Next, the dynamic time warping method is used to measure the distance between singular value sequences of the current and baseline signals. A quantitative DI is finally defined using these distances. Two experimental studies are utilized to validate the effectiveness of the proposed method. Results show that the proposed method can reveal the rich information hidden in the Lamb wave signals and has better trend-matching performance in fitting the actual damage propagation trend than traditional methods.