Damage In Composite Structures Research Articles

Synergistically improving the edge-on impact damage resistance of composite laminates using multi-scale CNF/Z-pin toughening

Edge-on impact damage of composite structures has drawn significant attention from the engineering and academic communities because of the much more notorious damage caused than that by out-of-plane impacts. However, research focusing on improving the edge-on impact resistance of these structures has been limited. This paper proposed a synergistic toughening approach involving multi-scale carbon nanofiber (CNF) and Z-pin to improve the composite laminates' edge-on impact damage resistance. Through analysis of mechanical response, C-scan, optical and Scanning electron microscopy (SEM) photographs of unreinforced, CNF-reinforced, Z-pin-reinforced and CNF/Z-pin-reinforced laminates, the failure mechanisms of the four types of laminates and the synergistic toughening mechanism of CNF/Z-pin were revealed. The results show that the synergistic toughening of CNF/Z-pin could weaken the negative effects caused by Z-pin implantation and that CNF has the potential to increase the damage initiation energy threshold of the laminate. Furthermore, the toughening effect of Z-pin was significantly greater than that of CNF in damage propagation. Specifically, at 50 J impact energy, the damage area of CNF/Z-pin, Z-pin and CNF-reinforced laminates was reduced by 50.5 %, 39.8 %, and 8.3 %, respectively, compared to unreinforced laminates.

Influence of modelling hypotheses on strength assessment of CFRP stepped repairs

Composite stepped repairs can achieve high strength recovery without the addition of bolts or fasteners to the structure. They are therefore a major issue in the field of aerospace composite structure damage repair. However, there is no standardized method to design this type of repairs. Many analytical, semi-analytical and finite element models were proposed throughout the years to predict the strength of stepped repairs, using various hypotheses and simplifications. The aim of this paper is to investigate the influence of modelling hypotheses on stress distribution and strength prediction of composite stepped repairs. Five simplified stepped joint models using macro-element (ME) modelling and finite element method (FE) are compared to a full 3D FE model of a stepped repaired panel. The influence of step length and adhesive fracture toughness was investigated to determine the field of validity of each model. Among FE models, it was shown that modelling the equivalent joint under 2D generalized plain strain gives a very close strength prediction to the 3D stepped repair model while saving computation time. Simplified macro-element models under bar or beam hypotheses are fairly close to the results of FE modelling, but the deviation between those and FE is sensitive to step length and adhesive fracture toughness.

Damage tolerance analysis of double strap joints with composite adherends

ABSTRACT Damages in composite structures are typically repaired using external bonded patches. In this work, the damage tolerance within external patches is studied using double strap joint (DSJ) with carbon fibre reinforced plastic (CFRP) specimens. The damage tolerance in this work refers to bond-line manufacturing-related defects such as debond or dry area. Both intact and pre-cracked DSJ specimens are numerically analysed using experiments for comparisons. DSJ specimens’ adherends were CFRP laminates with AS4/3501–6 plies and bonded using adhesive film FM 300–2. The pre-cracked specimens are monitored during experiments using high-speed digital image correlation to visualize damage propagation. The unsymmetric pre-crack influences in a clear way as shown by experiments. The numerical analyses reveal the effect of failure mechanisms on final failure and the maximum force during a test. The included parametric sensitivity analysis is performed using statistical methods. The normally distributed parameters are sampled using separate distributions for each analysed interface and fracture mode. UNCLASSIFIED Based on Foreground Information under EDA Contract No B.PRJ.RT.670 covering the Ad Hoc Project entitled ”PATCHBOND II”.

Acoustic emission detection of filament wound CFRP composite structure damage based on Mel spectrogram and deep learning

This paper investigates the hoop tensile mechanical properties, damage mode classification, and damage evolution process of the filament wound CFRP composite structure with different winding angles by acoustic emission and deep learning. Firstly, a clear correspondence between damage modes and peak frequency ranges is established by using k-means clustering results and eight frequency band ranges obtained by wavelet packet transform. On this basis, three damage modes are characterized according to the energy content of different frequency bands. Subsequently, the trained Mel spectrum-CNN classification model is used to identify damage modes and analyze the damage evolution process. The results show that the hoop tensile strength of the filament wound CFRP composite structure with ±55° is higher than that with ±35°. The primary damage modes during the tensile process are matrix cracking and fiber/matrix debonding. The Mel spectrogram-based damage identification method can effectively classify damage modes. In addition, the combination of acoustic emission and deep learning shows significant potential for damage identification and monitoring.

The temperature effects on embedded PZT signals in structural health monitoring for composite structures with different thicknesses

Ultrasonics guided waves (GWs) are established as an effective structural health monitoring technique for detecting damage in composite structures. However, for GWs to be applied reliably in service, the effect of temperature needs to be accounted for baseline damage detection approaches. Previous studies have focused on developing temperature compensation methods for surface-mounted transducers. In this paper, an extensive investigation has been carried out to develop an effective temperature compensation method for GWs generated by embedded transducers in different thickness composites. To accomplish this, Laser Doppler Vibrometer experiments were carried out to obtain wavenumbers generated by embedded transducers and to compare them against surface mounted cases. The consistent trend observation of dispersion curves between surface-mounted and embedded PZTs serves as a foundation for conducting analysis of the embedded signals with the effect of temperature. Following that, GW propagation has been studied in three quasi-isotropic composites of 2 mm, 4 mm and 9 mm thicknesses. Based on the established temperature database with measurements from − 50 °C to 70 °C, a temperature compensation methodology has been developed and validated with the temperature difference of 30 °C. It demonstrates that the temperature effect on the embedded PZTs is lower in comparison to surface mounted PZTs, and the temperature compensation of a single thickness composite is scalable to other panels thickness. This novel methodology proves beneficial in reducing the scale of the experimental campaign required, which holds significant value for actual aeronautical structures characterized by variable composite thicknesses across different sections, such as in fuselage panels or wing boxes.

Detection of Barely Visible Impact Damage in Composite Structures Using Backward Sweep Vibro-thermography Technique Utilizing Asymmetry in Local Defect Resonance

ABSTRACT In this article, local defect resonance-based vibrothermography has been studied for different sweep direction and ranges. Two different carbon fiber-reinforced polymer (CFRP) composite plate has been fabricated from vacuum assisted resin transfer molding. In the first plate, a flat bottom hole has been made and two barely visible impact damages are created in other plate. The area of delamination has been determined from phased array ultrasound testing. Laser doppler vibrometry (LDV) has been performed first for different sweep ranges and directions. The CFRP plate is excited with a piezoelectric element at 150 Vpp and the vibration over defect area is captured using a single point laser doppler vibrometer, operating in scanning mode. It has been found that vibration amplitude at local defect resonance (LDR) frequency increases in narrow sweep range compared to a wideband excitation. Again, in both cases, it has been found that backward sweep produces more amplitude compared to forward one due to softening nonlinearity. An asymmetry in LDR frequency is also been observed when the sweep range is further narrowed. An uncooled microbolometer camera is used for reception in case of vibrothermography. Backward sweep is found to be more effective as compared to the forward one and the temperature increment increases in case of narrowband excitation range.

Smart hybrid composite sensor technology to enhance the detection of low energy impact damage in composite structures

Smart hybrid composite sensor technology to enhance the detection of low energy impact damage in composite structures

Multi-Damage Detection in Composite Space Structures via Deep Learning.

The diagnostics of environmentally induced damages in composite structures plays a critical role for ensuring the operational safety of space platforms. Recently, spacecraft have been equipped with lightweight and very large substructures, such as antennas and solar panels, to meet the performance demands of modern payloads and scientific instruments. Due to their large surface, these components are more susceptible to impacts from orbital debris compared to other satellite locations. However, the detection of debris-induced damages still proves challenging in large structures due to minimal alterations in the spacecraft global dynamics and calls for advanced structural health monitoring solutions. To address this issue, a data-driven methodology using Long Short-Term Memory (LSTM) networks is applied here to the case of damaged solar arrays. Finite element models of the solar panels are used to reproduce damage locations, which are selected based on the most critical risk areas in the structures. The modal parameters of the healthy and damaged arrays are extracted to build the governing equations of the flexible spacecraft. Standard attitude manoeuvres are simulated to generate two datasets, one including local accelerations and the other consisting of piezoelectric voltages, both measured in specific locations of the structure. The LSTM architecture is then trained by associating each sensed time series with the corresponding damage label. The performance of the deep learning approach is assessed, and a comparison is presented between the accuracy of the two distinct sets of sensors: accelerometers and piezoelectric patches. In both cases, the framework proved effective in promptly identifying the location of damaged elements within limited measured time samples.

Detection of changes in dynamic characteristics of composite structures using Kriging metamodeling procedure: Experimental and computational analysis

To ensure the quality, safety, and long-term durability of fiber-reinforced composite components, it is necessary to explore approaches for monitoring and detecting defects or damage. Nondestructive detection techniques based on dynamic properties and structural response, such as natural frequency, damping, and modal shape. This study presents a numerical-experimental methodology based on dynamic analysis of structures made of composite materials. The methodology utilizes a surrogate model to establish a design envelope through a Kriging metamodel and to assess a damage index for structures affected by impact damage. The Latin Hypercube method is employed to generate values for the input variables, while the finite element method is utilized to calculate the natural frequencies. The Kriging metamodel is then employed to generate a numerical model, which is optimized using the Efficient Global Optimization algorithm and the expected improvement metric to minimize computational costs. The methodology yields a frequency range and determines a design envelope to evaluate the manufacturing quality of the structure. A damage index is used to identify structures with defects or impact damage, allowing for the assessment of severity. Additionally, the study evaluates the impact of incorporating metrics into the Latin Hypercube method to further reduce computational costs. Finally, this proposed approach contributes to the development of monitoring systems for assessing the manufacturing quality of composite structures and detecting impact damage through dynamic analysis. By utilizing this methodology, it becomes possible to effectively identify and evaluate the severity of defects and damage in composite structures, thus enhancing quality evaluation processes.

Study of multiple impact behaviors of CFRP based on peridynamics

Impact damage of carbon fiber reinforced polymers (CFRP) composites is a common form of damage in composite structures. In this work, a model integrating the structure model of composites, failure model and contact model with friction based on ordinary state-based peridynamics (PD) theory is developed to simulate multiple impacts of CFRP. The simulation results of single impact are in good agreement with the existing experimental and finite element simulation results, validating the proposed PD model. Then the effects of impact energy, stacking sequences and impact angles on the CFRP damage behaviors under multiple impacts are thoroughly investigated. Multiple high energy impacts may cause a non-linear increase in the amount of damage. The peanut-shaped stress distributions induced by impact along different directions are typical of the stress responses, which usually correspond to different fiber directions. Moreover, the laminates with stacking sequences of different fiber directions have a better impact damage resistance, as they can hinder the propagation of stress waves across the layers. In addition, as the impact angle increases, the stress and damage become more pronounced. The insights gained shed light on the repeated impact damage mechanisms of CFRP.

Quantitative monitoring of impact damage to composite structures using blade coated MXene sensors

Low velocity impacts usually cause invisible damage of composite structure，which is hard to be found and will cause catastrophic failure. In this work, the blade-coated MXene sensor was used to monitor impact damage of composite laminates. For the purpose of impact monitoring, an experiment was designed where composite laminates were subjected to controlled energy impacts. A circular sensor with a diameter of 100 mm was arranged on the back of the laminate. And ultrasonic C-scan was used to examine delamination damage of laminates after each impact. The experimental results show that the sensor identifies the onset of impact and the generation of damage. And the relative resistance changes of the sensor have a directly linear proportional relationship to the delamination size. There is also a functional relationship between the electrical response of the sensor and the impact energy. The findings are of great significance for the application of composite structure, and can effectively avoid the safety hazards caused by invisible impact delamination damage.

Damage localization for composite structure using guided wave signals with Gramian angular field image coding and convolutional neural networks

Accurate detection of damage in composite structures is of great significance to ensure safe service and avoid catastrophic accidents. In this paper, a novel damage diagnosis method, integrating guided wave (GW)-based structural health monitoring (SHM) with Gramian angular field (GAF) image coding and convolutional neural networks (CNNs), is proposed to improve the accuracy of damage localization in composite structures. Firstly, an improved piecewise aggregate approximation (PAA) algorithm is proposed to achieve guided wave data compression and obtain a series of damage indexes (DIs) characterizing the damage for effectively improving the localization accuracy. Then, the multi-path fused DI sequences are converted into two-dimensional (2-D) images using GAF to take full advantage of the significant benefits of CNN in machine vision. Secondly, a CNN model is constructed to learn high-level feature representations and conduct damage location regression. Finally, the proposed method is tested and assessed on measured GW signal datasets with the addition of zero-mean Gaussian noise. The experimental results show that the mean relative error of the proposed method for damage localization on the invisible datasets is 3.59%. The proposed method demonstrates better generalization and localization performance than other selected state-of-the-art damage localization techniques.

Delamination and Skin-Spar Debond Detection in Composite Structures Using the Inverse Finite Element Method

This work presents a novel strategy for detecting and localizing intra- or inter-laminar damages in composite structures using surface-instrumented strain sensors. It is based on the real-time reconstruction of structural displacements using the inverse Finite Element Method (iFEM). The iFEM reconstructed displacements or strains are post-processed or 'smoothed' to establish a real-time healthy structural baseline. As damage diagnosis is based on comparing damaged and healthy data obtained using the iFEM, no prior data or information regarding the healthy state of the structure is required. The approach is applied numerically on two carbon fiber-reinforced epoxy composite structures: for delamination detection in a thin plate, and skin-spar debond detection in a wing box. The influence of measurement noise and sensor locations on damage detection is also investigated. The results demonstrate that the proposed approach is reliable and robust but requires strain sensors proximal to the damage site to ensure accurate predictions.

Monitoring of Damage in Composite Structures Using an Optimized Sensor Network: A Data-Driven Experimental Approach.

Due to the complexity of the fracture mechanisms in composites, monitoring damage using a vibration-based structural response remains a challenging task. This is also complex when considering the physical implementation of a health monitoring system with its numerous uncertainties and constraints, including the presence of measurement noise, changes in boundary and environmental conditions of a tested object, etc. Finally, to balance such a system in terms of efficiency and cost, the sensor network needs to be optimized. The main aim of this study is to develop a cost- and performance-effective data-driven approach to monitor damage in composite structures and validate this approach through tests performed on a physically implemented structural health monitoring (SHM) system. In this study, we combined the mentioned research problems to develop and implement an SHM system to monitor delamination in composite plates using data combined from finite element models and laboratory experiments to ensure robustness to measurement noise with a simultaneous lack of necessity to perform multiple physical experiments. The developed approach allows the implementation of a cost-effective SHM system with validated predictive performance.

A global-local damage localization and quantification approach in composite structures using ultrasonic guided waves and active infrared thermography

The paper emphasizes an effective quantification of hidden damage in composite structures using ultrasonic guided wave (GW) propagation-based structural health monitoring (SHM) and an artificial neural network (ANN) based active infrared thermography (IRT) analysis. In recent years, there has been increased interest in using a global-local approach for damage localization purposes. The global approach is mainly used in identifying the damage, while the local approach is quantifying. This paper presents a proof-of-study to use such a global-local approach in damage localization and quantification. The main novelties in this paper are the implementation of an improved SHM GW algorithm to localize the damages, a new pixel-based confusion matrix to quantify the size of the damage threshold, and a newly developed IRT-ANN algorithm to validate the damage quantification. From the SHM methodology, it is realized that only three sensors are sufficient to localize the damage, and an ANN- IRT imaging algorithm with only five hidden neurons in quantifying the damage. The robust SHM methods effectively identified, localized, and quantified the different damage dimensions against the non-destructive testing-IRT method in different composite structures.

Characterization of Tensile and Fatigue Damages in Composite Structures Using Lamb Wave for Improved Structural Health Monitoring

The majority of ultrasonic characterizations are done on thermoplastics, with just a few articles available on the characterization of thermoset resin characteristics. A non-destructive methodology for monitoring fatigue and static deformation induced by mechanical loading on a fiber-reinforced plastic is presented. However, these materials’ dynamics of elastic waves are considerably more complicated. A large part was devoted to the calculation of dispersion curves of guided waves in composites. Therefore, this study presents a thorough description of the Glass/Epoxy system by comparing ultrasonic and mechanical data. Ultrasonic wave propagation at high frequencies, functioning as a dynamic mechanical deformation, may be utilized to calculate longitudinal and shear moduli during static and dynamic loading. The evolution of attenuation and velocity during loading is linked to the significant changes that occur during the aging process. The experimental transfer function is determined by the Fourier transform of all the obtained ultrasonic echoes.

Quantitative identification of damage in composite structures using sparse sensor arrays and multi-domain-feature fusion of guided waves

Damage detection techniques using Lamb waves have shown excellent capabilities in the diagnosis of composite structures. However, structural health monitoring of composite structures is challenging, especially for damage classification. This study proposes a machine learning-based method with a sparse sensor array to achieve quantitative classification of the damage location and severity on a composite plate. First, multi features extraction is used to construct a support vector machine (SVM) damage localization model. Second, optimal path extraction combined with principal component analysis (PCA) is used to construct an SVM model for classification. To reduce the operational burden of structures, the sparse array is employed. To improve the damage classification accuracy, Fisher clustering is proposed to extract the optimal detection path. Then, PCA is used to achieve data fusion. Experimental results on a glass fiber-reinforced epoxy composite laminate plate demonstrate that the proposed technique can accurately locate and classify the quantitative artificial damage.

A novel index for vibration-based damage detection technique in laminated composite plates under forced vibrations: experimental study

In this study, vibration modal analysis has been used to experimentally investigate the detection of fiber breakage (FB) and delamination in laminated composite plates. The modal analysis was performed on two arrangement styles of carbon fiber reinforced polymer (CFRP) laminated plates under forced vibrations. The main contribution in this study is that a new index named Improved Curvature Damage Factor (ICDF) was introduced to detect and localize damages in composite structures. It is a developed version of an earlier index based on the change in mode shape curvature (MSC) of dynamic response. To include the effect of more than one mode shape Cumulative Improved Damage Factor (CICDF) was proposed. The ICDF index was validated with a number of earlier studies that investigated the damage detection in different structures in order to approve the efficiency of this index. A scanning laser vibrometer was also used to experimentally examine the modal-based and forced responses for intact and damaged plates. Various experimental tests were used to verify the advantages of the proposed methodology and its use in structural health monitoring. In addition, the analysis of experimental results showed that the mutation caused by FB is almost double that of the value of delamination peaking.

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.

Damage in hybrid corrugated core sandwich structures under high velocity hail ice impact: A numerical study

Potential damage in composite structures caused by hail ice impact is an essential safety threat to the aircraft in flight. In this study, a nonlinear finite element (FE) model is developed to investigate the dynamic response and damage behavior of hybrid corrugated sandwich structures subjected to high velocity hail ice impact. The impact and breaking behavior of hail are described using the FE-smoothed particle hydrodynamics (FE-SPH) method. A rate-dependent progressive damage model is employed to capture the intra-laminar damage response; cohesive element and surface-based cohesive contact are implemented to predict the inter-laminar delamination and sheet/core debonding phenomena respectively. The transient processes of sandwich structure under different hail ice impact conditions are analyzed. Comparative analysis is conducted to address the influences of core shape and impact position on the impact performance of sandwich structures and the corresponding energy absorption characteristics are also revealed.