Health Monitoring Of Composite Materials Research Articles

Structural damage classification in composite materials using the Wigner-Ville distribution and convolutional neural networks

Detecting damage in composite materials is crucial and has received considerable attention in the field of machine learning. However, challenges remain in addressing backbone issues and classifying specific types of damage. This paper presents a novel deep-learning approach for automatically distinguishing delamination and microcracks in Carbon Fiber-Reinforced Polymer (CFRP). The methodology utilizes signals from piezoelectric transducers transformed into time–frequency representations based on the Wigner-Ville Distribution (WVD). Backbone issues were successfully addressed by transitioning the time series classification problem into a computer vision (CV) context through Convolutional Neural Networks (CNN). The analysis included a thorough examination of delamination and microcracks datasets produced experimentally by the National Aeronautics and Space Administration (NASA), focusing on these two types of damage. The proposed methodology achieved a precision range of 98.3 % to 100 % in damage classification, demonstrating its effectiveness for structural health monitoring in composite materials.

5G-enabled, battery-less smart skins for self-monitoring megastructures and digital twin applications.

With the current development of the 5G infrastructure, there presents a unique opportunity for the deployment of battery-less mmWave reflect-array-based sensors. These fully-passive devices benefit from having a larger detectability than alternative battery-less solutions to create self-monitoring megastructures. The presented 'smart' skin sensor uses a Van-Atta array design enabling ubiquitous local strain monitoring for the structural health monitoring of composite materials featuring wide interrogation angles. Proof-of-concept prototypes of these 'smart' skin millimeter-wave identification tags, that can be mounted on or embedded within common materials used in wind turbine blades, present a highly-detectable radar cross-section of - 33.75 dBsm and - 35.00 dBsm for mounted and embedded sensors respectively. Both sensors display a minimum resolution of 202 -strain even at 40 off-axis enabling interrogation of the fully-passive sensor at oblique angles of incidence. When interrogated from a proof-of-concept reader, the fully-passive, sticker-like mmID enables local strain monitoring of both carbon fiber and glass fiber composite materials. The sensors display a repeatable and recoverable response over 0-3000 -strain and a sensitivity of 7.55 kHz/ -strain and 7.92 kHz/ -strain for mounted and embedded sensors, respectively. Thus, the presented 5G-enabled battery-less sensor presents massive potential for the development of ubiquitous Digital Twinning of composite materials in future smart cities architectures.

Comparative analysis for strain response and life monitoring of composite using carbon-based nanosensors

Sensing technology based on carbon nanomaterials has become a very promising technology in the field of composite health monitoring, which requires high-sensitivity piezoresistive strain sensors to serve. In this study, two different carbon nano-sensors were used. Based on the traditional carbon nano-paper sensor, a flexible printed circuit and a polysulfone membrane were added to ensure the stability and sensitivity of the sensor. The conductive mechanism of adjacent carbon nanotubes (CNT) was analyzed. The electrical network model inside the carbon nanosensor was established, and its working mechanism during loading and unloading was described. The research mainly adopts three-point bending experiment. The deformation and failure of composite laminates containing two kinds of sensors under load experience low strain and high strain cyclic loading. Both sensors have the same response and stability as the load growth trend. Experiments show that the CNT-Paper sensor is suitable for long-term health monitoring of composite materials. The carbon nanotube-ultrafiltration membrane (CNT-UM) sensor is more sensitive to the cyclic monitoring of small strain.

Mechanical enhancement and high linearity health monitoring of composite materials based on CNTs/PSF/PI film sensor with ultra-low SWCNTs doping content.

A new type of embedded composite material health monitoring nano-sensor is designed to ensure that the unique material advantages of nanofillers can be maximized. The carbon nanotubes (CNTs)/polysulfone (PSF)/polyimide (PI) thin film sensor in this paper is obtained by the self-assembly of a PSF/PI asymmetric porous membrane which is prepared by a phase inversion method through vacuum filtration of SWCNTs. It is a new structure for a practical CNT sensor that can take into account both 'composite health monitoring and damage warning' and 'composite mechanical enhancement'. The new structure of the CNTs/PSF/PI film sensor is divided into two parts. The upper part consists of small-aperture finger-like holes filled with SWCNTs (the SWCNT content is 0.0127 mg cm-2). The lower part consists of large-aperture cavities conducive to resin infiltration, which enhance the interface bonding force between the sensor and the composite material. This unique structure allows the CNTs/PSF/PI film sensor to change the influence of the embedded sensor from 'introducing defects' to 'local enhancement', and the mechanical strength of the enhanced specimen can reach up to 1.68 times that of the original specimen, and the service interval can reach 2.01 times that of the original specimen. In addition, the CNTs/PSF/PI film sensor also has good sensitivity (GF = 2.54) and extremely high linearity (R2 = 0.9995), and has excellent follow-up and interface bonding ability. It can also maintain excellent fatigue resistance and stability over 46 500 vibration cycles, which provides new research ideas and research methods for the field of composite-life monitoring sensors.

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.

Deep Autoencoder Thermography for Defect Detection of Carbon Fiber Composites

Infrared thermography (IRT) is an economical nondestructive testing technique for structural health monitoring of composite materials. However, the nonlinear nature of the thermographic data and the adverse effects of noise and inhomogeneous backgrounds prevent it from achieving satisfactory results. Most of the existing thermographc data analysis methods are supervised and/or linear, which therefore are not favorable for nonlinear feature extraction of unlabeled thermograms. In this work, a deep autoencoder thermography (DAT) method is proposed for detecting subsurface defects in composite materials. The multi-layer network structure of DAT can handle nonlinear temperature profiles, and the output of the intermediate hidden layer is visualized to highlight defects. The layer-by-layer feature visualization reveals how the model extracts defect features. A loss inflection point scheme is utilized to determine a suitable depth of the model. Moreover, a new quantitative index is proposed to compare the defect detectability of different methods.

A static load position identification method for optical fiber-composite structures based on particle swarm optimization- back Propagation neural network algorithm

Aiming at researching on health monitoring of composite materials, a static load position identification method for optical-fiber composite structures based on the Particle Swarm Optimization-Back Propagation (PSO-BP) neural network algorithm is proposed. Based on the 2 × 2 optical fibers-composite structures, the PSO-BP algorithm is used to establish a nonlinear mapping between the fiber output intensity and the position. At first, a three-layer BP neural network is established. The number of the hidden layer is 30. And then the PSO algorithm is used to optimize the initial weights and thresholds of the BP neural network. Finally, a BP neural network is built using optimized initial weights and thresholds. A total of 515 sets of data samples are collected by the experimental system, of which 500 sets are used for training and 15 sets are used for the final model prediction. Simulation results show that the Mean Square Error (MSE) of the static load position prediction based on the PSO-BP algorithm is 0.0485. Compared with the position prediction model established by the BP neural network, Radial Basis Function (RBF) neural network and Support Vector Regression Machine (SVRM), the PSO-BP neural network model has a higher accuracy. The proposed method has an important application value for the research of health self-diagnosis of composite structures.

Modal behavior of post low velocity impact flax/epoxy composite structures

Natural fibers are increasingly used for polymer composite intending to minimize the environmental impact. Bio-composite materials are increasingly being used in industrial transport structures, including aerospace and automotive. Natural fiber reinforces composites with equivalent performances of glass fiber composites, have higher amount of fiber, resulting in less pollution and much lighter weight, which reduces the fuel consumption. Also, they offer the ability to design complex parts and high mechanical properties structures. Barely visible impact damage (BVID) represent a serious threat to the efficiency of bio-composite materials. In this paper, modal analysis was used to investigate and evaluate the impact-induced damage of flax/epoxy composite plates. The vibratory behavior is an indicator of the structural health monitoring of composite materials. Natural frequency, damping loss factors and displacement pattern, named mode shapes, are studied in order to detect damage and anticipate perilous consequences through time.

Nondestructive evaluation of carbon-fiber composites using digital image correlation, acoustic emission, and optical based modal analysis

Composite materials are increasingly used in the wind industries. Damage detection and health monitoring of composite materials are challenging due to the complex internal structure and unique material properties. Digital image correlation (DIC) and acoustic emission (AE) are both used for damage detection in structures. In this work, DIC performs a full-field strain measurement on the surface of the carbon-fiber specimen while AE continuously monitors and records the AE signals generated from specimen subsurface structure failures. These health monitoring techniques are integrated and evaluated in this study to correlate surface strain measurements and acoustic emission measurements on carbon-fiber specimens. The AE measurement results show that there is a correlation between the occurrence of AE events and the timing of complete specimen failure. DIC with a high-speed stereo camera system is also adopted to extract the change in the resonance frequencies and displacement and strain mode shapes of the specimen during experiments in cyclic loading.

Optical fibre based sensors application to structural health monitoring in composite materials

Structural health monitoring (SHM) by means of embedded fibre optic (FO) sensors is able to perform diagnosis and prognosis of structural damages and defects. SHM system monitor structural status based on the acquired data resulting in the detection and characterization of damages and defects without necessity for the aircraft to be on ground. Optical fibre sensors are permanently installed in the structure and trigger maintenance actions only when a damage or defect of a particular size is detected. To demonstrate the potential of FO sensors technology and their application by means of embedded sensors to composite material aerostructures, manufacturing trials have been performed and the influence in mechanical behaviour of the sensors’ embedding has been studied by performing tests campaings, at coupon level, for experimental validation. This study is within the scope of SHERLOC, a project belonging to the Clean Sky 2 JU aeronautical research and development program, and of which main objective is to combine advanced Structural Health Monitoring (SHM) and smart repair technologies with a probabilistic design philosophy, and hence to develop new maintenance concepts to reduce the direct operative costs without lowering the operational safety of a composite fuselage.

Structural Health Monitoring of Composite Materials

Structural Health Monitoring of Composite Materials

Impact Force Reconstruction of Composite materials based on Improved Regularization Technology

In the structural health monitoring of composite materials, in order to solve the ill-posed problem of impact force reconstruction, regularization techniques are often used to deal with it. Due to the poor convergence of the traditional Tikhonov regularization method, in order to accurately reconstruct the time history of the impact force, this paper improves Tikhonov regularization method and constructs homotopy function with strong convergence. Since the optimal regularization parameters need to be found in the homotopy function, the Newton downhill method is used to find the optimal parameters and the homotopy function can be calculated, which can accurately reconstruct the time history of the impact force. In order to verify the universality of the method in this paper, impact hammers of different materials were used in the experiment in this paper to study and compare the reconstruction effect of impact time history of different impact hammers.

High-sensitive flexural sensors for health monitoring of composite materials using embedded carbon nanotube (CNT) buckypaper

The health monitoring for the service status of the composite materials is particularly crucial, calling for highly sensitive piezoresistive strain sensors. This paper outlined the working mechanisms of CNT buckypaper sensor during loading and releasing processes by analyzing the electrical conduction mechanism of the adjacent CNT and developing the macroscopical and mesoscopic models of CNT electrical networks inside of the bukypaper. The CNT-based nanocomposite specimens underwent monotonic and cyclic flexural loading in low strain range to examine the dynamic stability and durability of the in-situ nano sensor. The different support span configurations were also employed and operated to obtain the recommended application scenarios. The long-term response behavior of the nano sensor was also concluded as three typical phases. The desirable agreement was demonstrated by comparing the responses of ΔR/R0 signals and phenomena of the breakage and propagation of micro cracks and the rearrangement and reconnection of CNT conductive networks within the Scanning electron microscope (SEM) images of the buckypaper. The proposed flexural nano sensor is suitable for potential health monitoring application of composite materials.

Graphene and carbon nanotube-based high-sensitive film sensors for in-situ monitoring out-of-plane shear damage of epoxy composites

Graphene (GN) and carbon nanotube (CNT) have already been explored their potential crack-monitoring abilities in the field of composite materials, but a relatively low sensitivity to shear damage is still a practical problem. This work aims to improve the monitoring sensitivity to out-of-plane shear damage by constructing hybrid GN/CNT film sensors with a GN weight loading of 0–10%. The prepared GN/CNT sensors are integrated into epoxy composites and both quasi-static and cyclic shear loads are carried out. It is found from quasi-static testing that once the out-of-plane shear damage initiates, relative electrical resistance variation suddenly jumps about 34–191% from the GN/CNT film sensors. Also, the sensitivity coefficient in the crack growth stage could reach 124–684. Namely, the GN/CNT sensors show high monitoring sensitivity to the initiation and growth of out-of-plane shear damage. Moreover, results from cyclic loading further reveal that the GN/CNT sensors possess stable sensitivity to the shear deformation and damage. Meanwhile, similar response trends from an acoustic emission sensor prove the monitoring reliability of the GN/CNT film sensors. The obtained high sensitivity demonstrates that the GN/CNT hybrid film sensors are prospective in the field of structural health monitoring of composite materials.

Hybrid Fiber Optic Sensor Systems in Structural Health Monitoring in Aircraft Structures.

‘Smart’ structural health monitoring of composite materials with optical fiber sensors is becoming more and more important, especially in the aviation industry. This paper presents an overview of hybrid fiber-optic sensing systems based on scattering techniques, fiber Bragg gratings, interferometric techniques, and polarimetric methods in structural health monitoring. The main purpose of this manuscript is to analyze the possibilities of using hybrid sensors based on fiber optics to monitor composite structures, with a particular emphasis on aircraft structures. Since it is difficult to indicate the most comprehensive approach due to different parameters of the described sensors, the review contains a detailed description of available solutions. We hope that this work will allow for a better and faster selection of the right solution for the problem at hand.

Health monitoring of composite materials based on BP sensors under complex environments

In this study, the air thermal cycle test and the relative humidity (RH) cycle test were carried out to buckypaper (BP) co-cured with glass fibre prepreg together in order to verify the practicality and stability of the sensor in complex environment. The BP bonded with composite laminate were subjected to tension and tension–tension fatigue up to 1000 cycles when the sensor is subjected to temperature changed from 20 to 120°C and different RH from 0 to 85%, respectively, and the electrical–mechanical response shows that the resistance of sensor increases as the humidity increases, and ultimately the change rate tends to be stable. Tensile experiments shows that gauge factor (GF) changes by 327 and 306%, respectively. The authors finished the fatigue experiment to specimen of the longest environmental treatment in order to further verify the practicality of the sensors, in the fatigue test, the resistance change rate varies with the number of cycles, and it tends to be stable after a period of time. The authors draw the curve of GF and fatigue cycle number so that the stability of the sensors are better reflected, the result is consistent with the fatigue result. Therefore, BP sensors can be applied to the health monitoring of aerospace composites.

Tensile strain synergistic of carbon nanotube buckypaper composites

As carbon nanotube buckypaper composites have a good interfacial properties, conductivity, and thermal conductivity, they can be used for whole-life health monitoring of composite materials. In this paper, carbon nanotube buckypaper and its sensors have been prepared. Meanwhile, composites samples with the buckypaper sensors at different positions were obtained. Tensile loading and unloading tests were performed. By analyzing changes in the buckypaper resistance change rate and composite strain, a strain-sensing coefficient was obtained to clarify the synergistic deformation of composite materials. The results of this study demonstrate that buckypaper sensor can be used to monitor the tensile fatigue damage of composites.

Characterization of process-induced defects in automated fiber placement manufacturing of composites using fiber Bragg grating sensors

With the increasing use of automated fiber placement method for manufacturing highly precise bespoke composite components in the aerospace industry, the level of manufacturing defects within the laminate structure needs to be monitored and minimized for structural integrity. One of the main common defects in automated fiber placement process is misalignment between the tape paths in successive courses which leads to non-integrity of laminate and consequently significant reduction in mechanical strength of the laminate. Therefore, it is necessary to find an appropriate inspection method to monitor and identify these processing defects at the earlier stages of manufacturing. Since optical fiber Bragg grating sensors are being increasingly utilized for structural health monitoring in composite materials and as they were successfully implemented by Oromiehie et al. in their earlier work for on-line lay-up process monitoring, the same methodology is once again tried for identifying the misalignment defects in automated fiber placement process. The experiments are carried out on glass-fiber/nylon laminate with embedded fiber Bragg gratings for the automated tape placement method. The defects due to misalignment are identified by the fiber Bragg grating sensors through their reflected wavelength changes during the automated manufacturing process. The analysis of results indicates that the fiber Bragg grating sensors can be reliably implemented for on-line defect monitoring during the automated fiber placement process to ensure the quality of final product and maintain the expected design life.

Delamination characterization in composite laminates using acoustic emission features, micro visualization and finite element modeling

Acoustic Emission (AE) is a capable approach to characterize delamination initiation and propagation in laminated composite materials. One of the major issues of applying this methodology is to establish a reasonable correlation between delamination initiation and propagation and resultant AE features. To this aim, initiation and propagation of mode I delamination is investigated in glass/epoxy composite materials. Micro and macro observation and acoustical energy were used to develop novel AE-based approaches for investigation of delamination damage. The privilege of these novel approaches is to detect the exact time of initiation of delamination in different laminates which was the lack of previous literature. The Cohesive Zone Modeling (CZM) was used to simulate the delamination growth. Scanning Electron Microscope (SEM) images were also used to investigate damage mechanisms at the interface of crack growth. The results show that AE and CZM approaches present decent capability to characterize delamination. The output of this study could lead to enhancing automatic techniques for structural health monitoring of composite materials.

Biosensing by WGM Microspherical Resonators.

Whispering gallery mode (WGM) microresonators, thanks to their unique properties, have allowed researchers to achieve important results in both fundamental research and engineering applications. Among the various geometries, microspheres are the simplest 3D WGM resonators; the total optical loss in such resonators can be extremely low, and the resulting extraordinarily high Q values of 108–109 lead to high energy density, narrow resonant-wavelength lines and a lengthy cavity ringdown. They can also be coated in order to better control their properties or to increase their functionality. Their very high sensitivity to changes in the surrounding medium has been exploited for several sensing applications: protein adsorption, trace gas detection, impurity detection in liquids, structural health monitoring of composite materials, detection of electric fields, pressure sensing, and so on. In the present paper, after a general introduction to WGM resonators, attention is focused on spherical microresonators, either in bulk or in bubble format, to their fabrication, characterization and functionalization. The state of the art in the area of biosensing is presented, and the perspectives of further developments are discussed.