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Delamination is one of the detrimental defects in laminated composite materials that often arose due to manufacturing defects or in-service loadings (e.g., low/high velocity impacts). Most of the contemporary research efforts are dedicated to high-frequency guided wave and mode shape-based methods for the assessment (i.e., detection, quantification, localization) of delamination. This paper presents a deep learning framework for structural vibration-based assessment of delamination in smart composite laminates. A number of small-sized (4.5% of total area) inner and edge delaminations are simulated using an electromechanically coupled model of the piezo-bonded laminated composite. Healthy and delaminated structures are stimulated with random loads and the corresponding transient responses are transformed into spectrograms using optimal values of window size, overlapping rate, window type, and fast Fourier transform (FFT) resolution. A convolutional neural network (CNN) is designed to automatically extract discriminative features from the vibration-based spectrograms and use those to distinguish the intact and delaminated cases of the smart composite laminate. The proposed architecture of the convolutional neural network showed a training accuracy of 99.9%, validation accuracy of 97.1%, and test accuracy of 94.5% on an unseen data set. The testing confusion chart of the pre-trained convolutional neural network revealed interesting results regarding the severity and detectability for the in-plane and through the thickness scenarios of delamination.

This work is concerned with the dynamic thermo-electro-mechanical response of smart composite laminates (SCLs) integrated with homogeneous piezoelectric layers. It extends our earlier quasistatic model and accounts for the coupled dynamic behaviour of SCL; with the piezoelectric layers working as smart sensors or active actuators. In the current nonlinear dynamic model, the geometric nonlinearity is accounted for using von Kármán’s formulations and the displacement field is described using a third-order shear deformation hypothesis. The fully coupled thermo-electro-mechanical constitutive laws are applied using temperature dependent material properties. The resulting nonlinear system of partial differential equations is converted into a robust and efficient finite element (FE) model. The FE model is used to examine the thermal stress states, the natural frequencies and frequency responses of the SCL plate under different temperatures. The results of our extensive analysis show a decrease in the natural frequencies and increase in vibration amplitudes of the SCL plate with increased temperatures. This is due to the development of thermal stresses as well as the thermal sensitivity of the material properties. Additionally, our results reveal the thermal effects on the electric field, the voltage output of the piezoelectric sensors and the control efficiency of actuators.

This article presents a numerical study of active vibration control of smart composite laminates in the presence of actuator debonding failures. A comparison between the smart composite laminates with healthy actuator and various partially debonded actuator cases is performed to investigate the debonding effects on the vibration suppression. The improved layerwise theory with Heaviside’s unit step function is adopted to model the displacement field with actuator debonding failure. The higher order electric potential field is adopted to describe the potential variation through the thickness. The finite element method–based formulations are derived using the plate element, taking into consideration the electro-mechanical coupling effect. The reduced-order model is represented by the state-space form and further for the vibration suppression using a simple constant gain velocity feedback control strategy. For the purpose of demonstration, a 16-layer cross-ply substrate laminate ([0/90]4s) is employed for the numerical study. The results show that the actuator debonding affects the closed-loop frequencies, active damping ratios, and efficiency of vibration suppression.

In this work, we propose a principal component analysis and system identification–based failure assessment approach for evaluating the partial actuator debonding failures in smart composite structures. Actuator debonding failure changes the structural dynamic characteristics and reduces the actuation capabilities as well in smart composite structures. First, the modeling of actuator debonding in smart composite laminate is developed using the finite element method, which incorporates the improved layerwise theory and higher-order electric potential field for the electromechanical coupling. Second, the structural responses obtained from the developed modeling are fed into the system identification to identify the system parameters of both healthy and damaged systems. Third, the achieved system parameters are further used for the statistical analysis by principal component analysis to extract the failure-sensitive features. Finally, a numerical example is studied using a 16-layer cross-ply laminate ([0/90]4s) as the substrate with various actuator debonding sizes. The results show that the actuator debonding failures can be well assessed, and the failure intensity and location can also be evaluated using the proposed approach.

This study presents a comprehensive review of the history of research and development of different damage-detection methods in the realm of composite structures. Different fields of engineering, such as mechanical, architectural, civil, and aerospace engineering, benefit excellent mechanical properties of composite materials. Due to their heterogeneous nature, composite materials can suffer from several complex nonlinear damage modes, including impact damage, delamination, matrix crack, fiber breakage, and voids. Therefore, early damage detection of composite structures can help avoid catastrophic events and tragic consequences, such as airplane crashes, further demanding the development of robust structural health monitoring (SHM) algorithms. This study first reviews different non-destructive damage testing techniques, then investigates vibration-based damage-detection methods along with their respective pros and cons, and concludes with a thorough discussion of a nonlinear hybrid method termed the Vibro-Acoustic Modulation technique. Advanced signal processing, machine learning, and deep learning have been widely employed for solving damage-detection problems of composite structures. Therefore, all of these methods have been fully studied. Considering the wide use of a new generation of smart composites in different applications, a section is dedicated to these materials. At the end of this paper, some final remarks and suggestions for future work are presented.

Unidirectional fiber-reinforced composites of “plain” carbon fiber–reinforced polymer laminates and carbon nanofibers modified carbon fiber–reinforced polymer laminates were prepared based on the manufacture of the epoxy resin modified with various contents of carbon nanofibers. The carbon nanofibers–modified epoxy matrix and carbon fiber–reinforced polymer laminates specimens were subject to constant amplitude cyclic tensile loading, quasi-static tension loading, and incremental cyclic tension loading while the values of their electrical resistance were monitored through electrical resistance technique. Resistance-change curves of carbon nanofibers/carbon fiber–reinforced polymer laminates indicated the changes in conductive percolation networks formed by carbon fibers or carbon nanofibers. These changes can identify the complex damage modes and the loss of mechanical integrity in laminates. The changes in resistance of specimens showed a nearly linear correlation with the strain, so the damage process of the carbon fiber–reinforced polymer laminates can be self-sensed according to the resistance-change curves. In addition, uniformly dispersed carbon nanofibers formed a network that spans the whole insulation area, which improved their self-sensing property of strain sensitivity without compromising the mechanical properties of the carbon fiber–reinforced polymer laminates. This technology can achieve the quantitative strain and damage self-sensing properties of nano-reinforced composites without any additional sensor, and it is bound to be a promising method for in situ health monitoring.

Bistable laminates (BSLs) are prone to vibration and dynamical snap-through behavior (STB) under the action of external environment. To control them, active vibration control using smart material is a terrific choice because it can minimize the impact on the stable configuration and properties of bistable laminate. This paper focuses on the active vibration control of rectangular asymmetric and anti-symmetric cross-ply bistable laminates under impact loadings using piezoelectric macro-fiber composite (MFC) whose size and position of paste are optimized instead of pasting randomly or middle of the laminate. The bistable laminated structures are simply supported at four selected points, while all the edges of them are free. With the aid of energy principle, governing equations of vibration of the bistable laminated structure are acquired with regard to two principal curvatures. The accuracy and validation of present formulation are verified by comparison studies of stable configurations and snap-through voltage of MFC. Then, the positions and geometric dimensions of piezoelectric macro-fibers are optimized by using genetic algorithm. The active vibration control of the bistable laminated structures subjected to step loading, decreasing loading, increasing loading and sinusoidal loading is studied for various control gains, geometries and different simply supported points.

This critical review provides the first coherent elucidation of the science behind electrical-resistance-based strain/damage/temperature self-sensing in continuous carbon fiber polymer-matrix composites, which are important for lightweight structures (e.g., aircraft). Self-sensing pertains to smart structures. It is based on structure–property relationships. There is no device incorporation. The type of loading and the type of resistance measurement affect the resistance change. The resistance changes are consistently elucidated in terms of the structure–property relationships. Upon elastic flexure, the tension-surface resistance increases, due to the fiber waviness decrease and consequent current penetration decrease, while the compression-surface resistance decreases. Upon elastic through-thickness compression, the through-thickness resistivity decreases, due to the enhanced through-thickness fiber proximity, thus promoting the longitudinal current path detour necessitated by fiber imperfection and decreasing the longitudinal resistivity. For a single fiber in epoxy, the resistivity decreases upon tension, due to the decrease of the composite-fabrication-induced residual compressive stress in the fiber. For multi-lamina laminates, the residual compressive stress decrease upon elastic longitudinal tension causes the longitudinal resistivity to decrease, while the through-thickness resistivity increases due to the fiber waviness decrease. For 1-lamina laminates, elastic longitudinal tension decreases the relatively limited fiber waviness, thereby increasing the through-thickness/transverse resistivity and consequently hindering the longitudinal current path detour and increasing the longitudinal resistivity. Delamination decreases the interlaminar fiber touching, thereby increasing the through-thickness/interlaminar resistivity. Heating decreases the interlaminar resistivity, due to interlaminar electron jumping, thereby enabling temperature sensing. Fiber fracture and interlaminar degradation increase the resistance irreversibly. For sensing matrix cracking, conductive filler incorporation helps.

Over the past few decades, there has been a growing trend in designing multifunctional materials and integrating various functions into a single component structure without defects. This research addresses the contemporary demand for integrating multiple functions seamlessly into thermoplastic laminate structures. Focusing on NiTi-based shape memory alloys (SMAs), renowned for their potential in introducing functionalities like strain measurement and shape change, this study explores diverse surface treatments for SMA wires. Techniques such as thermal oxidation, plasma treatment, chemical activation, silanization, and adhesion promoter coatings are investigated. The integration of NiTi SMA into Glass Fiber-Reinforced Polymer (GFRP) laminates is pursued to enable multifunctional properties. The primary objective is to evaluate the influence of these surface treatments on surface characteristics, including roughness, phase changes, and mechanical properties. Microstructural, analytical, and in situ mechanical characterizations are conducted on both raw and treated SMA wires. The subsequent incorporation of SMA wires after characterization into GFRP laminates, utilizing hot-press technology, allows for the determination of interfacial adhesion strength through pull-out tensile tests.

The multiple coupling of composite laminates has a unique advantage in improving the macro mechanical properties of composite structures. A total of three hygro-thermally stable multi-coupled laminates with extension-twisting coupling were presented, which were conducive to the formation of passive adaptive structures. Then, the multi-coupled laminates were used to design the bending-twisting coupled box structure, in which the configuration of laminate and box structure could be extended to variable cross-section configuration. The optimal design of stacking sequence was realized, the optimization objectives of which were to maximize bending-twisting coupling of box structure and extension-twisting coupling of laminate, respectively. The effects of multiple coupling on hygro-thermal stability, coupling, failure strength, buckling load, robustness and other comprehensive mechanical properties of laminates and box structures were analyzed by parametric modeling method. The results show that the extension-twisting coupling of laminate and the bending-twisting coupling of box structures can be greatly improved by 450% and 260% at maximum, respectively. Meanwhile, it would have a negative impact on the failure strength and buckling load, which, however, can be minimized by a reasonable paving method. Multi-coupled laminates have good robustness, and the bending-twisting coupling helps improve robustness. Finally, the hygro-thermal stability and mechanical properties were verified by numerical simulation with finite element method.