Damage In Composite Materials Research Articles

FTnet: An integrated network for fusing multi-modal NDE data of lightning damage in aircraft composite materials

Lightning strikes pose a significant challenge for aircraft and wind turbine blades with Carbon Fiber Reinforced Polymer (CFRP) structures, requiring reliable damage detection techniques. Non-destructive evaluation (NDE) methods, including X-ray and Ultrasonic Testing, are effective in identifying material damage in aircraft. However, X-ray requires access to both sides of the structure, and UT requires a coupling medium between the transducer and the structure, as well as a relatively smooth surface, making both methods less feasible for routine aircraft maintenance. Other NDE techniques, such as eddy current testing and infrared thermography, can detect damage on the side struck by lightning but lack the precision needed for a comprehensive assessment. To address these challenges, this paper introduces a two-stage Fusion-Translation network (FTnet), which integrates NDE 4.0 innovations, including data fusion and advanced imaging algorithms, to optimize the NDE process. By integrating infrared and eddy current data, FTnet characterizes lightning-induced damage with enhanced depth and contour detail, demonstrating superior performance over existing methods in both qualitative and quantitative evaluations. The implementation of FTnet marks an advancement in NDE 4.0, potentially enhancing aircraft safety and streamline maintenance protocols by providing a more reliable and comprehensive assessment of lightning strike damage.

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.

Модель накопления повреждений в ортотропном композиционном материале

The paper presents a theoretical and experimental study of the influence of damage accumulation in composite material on the elastic fourth-rank tensor and on the strength under quasi-static, cyclic and dynamic loads. Layered polymer composites (glass or carbon fiber-reinforced plastic) are considered. This work aims to develop a mathematical model that accounts for the impact of damage in the composite material on the fourth-rank tensor of elastic properties. The constitutive relations for an orthotropic composite material are proposed, taking into account the accumulation of damage during tensile or shear loading. A numerical simulation using the finite element method is done for a periodicity cell for composite variants (unidirectional, layered, etc.) to determine the effective elastic properties. Quasi-static loading experiments were conducted on composite samples (carbon plastic) to validate the model and the degradation of elastic properties was determined by measuring the longitudinal sound speed. The full-scale experiments confirmed that damage accumulation in carbon fiber-reinforced plastic leads to the degradation of its effective elastic properties. In this connection, a technique was developed to take into account irreversible damage in an orthotropic composite material through the components of the ductility tensor, which may be helpful for the design and analysis of composite structures. The findings of the study could help with the creation of new materials with enhanced mechanical characteristics, as well as with raising the quality of already-existing materials. The developed mathematical model of orthotropic composite material can be used to enhance the structural strength properties and boost the application safety in a variety of industry.

Production of low-CO2 ternary binder using red sandstone, cement, and granulated blast furnace slag: A comprehensive performance analysis

Red sandstone is rich in reserves, and using it as a mineral admixture to partially replace cement can save cement consumption and reduce carbon dioxide emissions. This study uses spectral characterization, electron microscopy image analysis, mechanical property testing and other methods to study the fluidity, compressive strength, heat of hydration, carbonation degree, microstructure and thermal damage of composite cement-based materials containing red sandstone and slag. The experiment results showed that in ternary mixed cement, the composite cement mixed with 5% slag and 10% red sandstone shows similar flow properties to cement paste. Moreover, as the mineral content (red sandstone and slag) increases, the dissolution peak of the Al phase appears earlier, and the degree of carbonation increases. Furthermore, the CO2 emissions per unit strength gradually decrease. After 28 days of curing, thermal damage experiments demonstrated that the strength of the sample increases by more than 3.6% when the mineral content is greater than 15% at 200℃. Samples were subjected to 600 °C and 900 °C, and the strength of the red sandstone sample with a content of 15% decreased significantly. Moreover, as the temperature increases to (600 °C and 900 °C), the lattice of CnS becomes more and more ordered.

Experimental and Simulation Study on Low-velocity Impact Damage of Composite Laminates under Temperature Environment

The composite material casing of aero-engine operates in different temperature environments and is inevitably impacted by external objects during use. The resin matrix composite materials are sensitive to temperature, which leads to more complex damage propagation and failure modes of materials under temperature environment. The influence of temperature environment on low-velocity impact damage of composite materials needs to be studied. In this paper, the T300/QY8911 composite laminates were subjected to low-velocity impact test and simulation study under three different temperature environments of 20°C, 120°C, and 180°C. The impact damage detection was carried out by visual inspection and ultrasonic C-scan to analyze the influence of temperature on the damage of composite laminates. Finite element simulation analysis was also conducted on the gradual expansion process of impact damage of composite laminates under different temperature environments to predict the impact damage area of laminates. The results show that the main damage forms are matrix cracking and delamination and the temperature has a significant effect on the low-velocity impact damage of composite laminates. Under the same impact energy, the length of the back crack and the cracking damage of the matrix of the laminate decreases as the temperature increases; the impact damage area and the delamination damage of the laminate increases as the temperature increases.

Lamb wave-based damage assessment for composite laminates using a deep learning approach

With the increasing utilization of composite materials due to their superior properties, the need for efficient structural health monitoring techniques rises rapidly to ensure the integrity and reliability of composite structures. Deep learning approaches have great potential applications for Lamb wave-based damage detection. However, it remains challenging to quantitatively detect and characterize damage such as delamination in multi-layered structures. These deep learning architectures still lack a certain degree of physical interpretability. In this study, a convolutional sparse coding-based UNet (CSCUNet) is proposed for ultrasonic Lamb wave-based damage assessment in composite laminates. A low-resolution image is generated using delay-and-sum algorithm based on Lamb waves acquired by transducer array. The encoder-decoder framework in the proposed CSCUNet enables the transformation of low-resolution input image to high-resolution damage image. In addition, the multi-layer convolutional sparse coding block is introduced into encoder of the CSCUNet to improve both performance and interpretability of the model. The proposed method is tested on both numerical and experimental data acquired on the surface of composite specimen. The results demonstrate its effectiveness in identifying the delamination location, size, and shape. The network has powerful feature extraction capability and enhanced interpretability, enabling high-resolution imaging and contour evaluation of composite material damage.

Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials

Most existing machine learning approaches for analysing thermograms mainly focus on either thermal images or pixel-wise temporal profiles of specimens. To fully leverage useful information in thermograms, this article presents a novel spatiotemporal-based deep learning model incorporating an attention mechanism. Using captured thermal image sequences, the model aims to better characterise barely visible impact damages (BVID) in composite materials caused by different impact energy levels. This model establishes the relationship between patterns of BVID in thermography and their corresponding impact energy levels by learning from spatial and temporal information simultaneously. Validation of the model using 100 composite specimens subjected to five different low-velocity impact forces demonstrates its superior performance with a classification accuracy of over 95%. The proposed approach can contribute to Structural Health Monitoring (SHM) community by enabling cause analysis of impact incidents based on predicting the potential impact energy levels. This enables more targeted predictive maintenance, which is especially significant in the aviation industry, where any impact incidents can have catastrophic consequences.

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

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

Effect of stacking angles on crashworthiness of CFRP square tubes considering low-velocity impact damage

To enhance the residual energy‐absorption (EA) of low-velocity impact-damaged CFRP square tubes, this study investigates the influence of stacking angles on the damaged tubes' residual EA. Through simulation and theoretical analysis, a well-designed stacking angles is proposed to optimize the residual EA. Initially, a simplified method of low-velocity impact damage of composite materials is proposed, by which a discontinuous damage mechanics finite element model is established and experimentally verified. Subsequently, various layup sequences are examined, and their impact on energy absorption is evaluated using several performance indices, including initial peak load, average load, energy absorption, specific energy absorption, and crushing load efficiency. The findings demonstrate that the designed stacking angles significantly enhances the residual energy absorption of low-velocity impact-damaged square tubes. Moreover, the study verifies the efficacy of the stacking angles design by investigating actual damage conditions, encompassing factors such as damage size, location, and stress concentration.

Characterization of Crack Damages in Composite Materials by Using Frequency- and Time-Domain Analysis

Characterization of Crack Damages in Composite Materials by Using Frequency- and Time-Domain Analysis

Inspection of Surface Damage in Composite Materials with Different Techniques

Due to their excellent physical properties and high strength and stiffness relative to density, aerospace industry research is producing high-performance structural materials, such as composites, which are used in many critical structural parts like airframes, wings, rotor blades, propellers, and other components. However, during flight, these materials may be damaged by impact, thermal stress, moisture, and ultraviolet radiation. One of the most prevalent issues with composite materials is their challenging nature in terms of flaw detection during both manufacturing and use. When they are employed in the crucial areas that were previously indicated, this becomes a very serious issue. When evaluating the structural integrity of composites and looking for any damage, microscopes are a very useful instrument. Effective methods for identifying and analyzing damage include microscopic procedures like optical microscopy, stereomicroscopy, scanning electron microscopy (SEM), scanning ion microscopy (SIM), and atomic force microscopy (AFM). A variety of methods may be employed with microscopes to examine and identify deterioration in composite materials. It is often possible to examine overt deterioration on the surface of composite materials under the microscope utilizing a number of different approaches and procedures. Determining the kind, extent, distribution, and impact of the damage requires these inspections. Often employed techniques consist of: SEM is a method for high-resolution imaging of surface damage. It entails shining an electron beam onto the sample's surface and capturing pictures. SEM is a useful tool for identifying erosion, delamination, and microcracks. It is also possible to measure things like the damage's breadth and depth. Optical microscopes have a large field of view and look at damaged regions. This makes it possible to find tiny fractures or cracks that are invisible to the unaided eye. Furthermore, details on the degree of harm, the roughness of the surface, and the breadth and depth of the fractures may be acquired. To see damaged objects, optical microscopy is utilized. Cracks and damage locations are visible with optical microscopy. Optical microscopes can identify different kinds of damage by looking at the surface of the material. Damage like delamination, fiber breakage, cracks, and deformations are a few examples of these. This study examines the efficacy of microscopic methods and non-destructive testing in assessing the different kinds of damage that can occur at the interfaces between holes in composite materials. Composite test materials were chosen from glass fiber reinforced phenolic matrix composites that were produced in compliance with aerospace standards. The measurements led to the conclusion that using microscopic techniques has benefits like speed and field suitability. However, the continuous development and improvement of new methods in this field will contribute to a better understanding of layered composite materials and the development of safer and more durable structures.

Scalable and passive carbon nanotube thin-film sensor for detecting micro-strains and potential impact damage in fiber-reinforced composite materials

Structural health monitoring (SHM) of brittle structures will require versatile sensing instrumentation that can transmitting transient loadings into rapid electrical responses. This research investigated the sensing performance of CNT Buckypaper (CNT-BP) thin-films on stressed fiber-reinforced composites, which are integral materials to various industries of the infrastructure. Three-point bending experiments revealed an extraordinary gauge factor (∼40) and impressive response linearity. Electrical responses were instantaneous; moreover, low force impacts were detectable, the sensors provided clear indications of spatial recognition. Most importantly, the manufacturing methods are scalable and cost effective compared to commercialized strain gauges. This study examined micro-strain measurements (<1%) of carbon fiber composites, in which bending strain of 0.002% could be detected. In addition, damage progressions of glass fiber composites were recorded in response to low-impact energies. Less than 20 J of impact energy was detectable by the sensor, and the severity could be assessed from drastic changes in the sensing behavior.

A Review of Delamination Damage of Composite Materials

The theoretical and practical achievements in the field of the theory of strength and reliability of composite materials are discussed in a review conducted on the scientific research conducted on the effect of delamination on the reliability and quality of composites. The methodological aspects of the stability of the mechanical characteristics of composite materials under the combined action of cyclic and impact loads are examined, as are the manufacturing and processing technologies. The reasons for delamination, such as technological, manufacturing and application, free edge, joints and loads, are revealed. The influence of delamination on the bearing capacity of structural elements made of composite materials is analyzed. The mechanism of delamination growth is outlined, and the criteria and processes are defined, such as the growth of delamination cracks in a multidirectional laminated plate from a straight edge, edge delamination during plate bending, delamination in plates in the field of residual stresses, etc. The importance of taking into account the visco-plastic effect at the top of the edge crack of delamination of composite materials is emphasized. The concept of critical delamination behavior is characterized, and the issues of delamination stability are described.

Acoustic emission study on damage mechanism of composite materials repaired with bi-adhesive and damage classification model

The load-bearing capacity of damaged composite materials can be improved using the double adhesive repair technique. This study uses acoustic emission (AE) as the primary detection method to examine the damage evolution of modified double-bonded repair specimens and discover the most effective configuration for bi-adhesive repair. The introduction of machine learning and signal feature extraction in the analysis of huge acoustic emission parameters has significantly improved the accuracy and efficiency of damage classification. Following the preliminary division of the clustering results of the unsupervised algorithm, the corresponding relationship between various clustering types and damage types is confirmed using the energy characteristics of wavelet packet decomposition. Parameter and waveform analysis are extensively utilized in studying the AE damage of composite materials and have complementary advantages. Thus, a supervised classifier is established by combining characteristic parameters with time-frequency domain analysis of the signal waveform. Furthermore, machine learning is used to process the large signal set and analyze the debonding damage behavior of the patch. In the end, it is shown that the specimens with the modified adhesive in the middle of the repair area displayed superior repair results. Hence, when combined with machine learning, the signal classification of the damage evolution behavior of repaired specimens is realized with high accuracy.

Buckling and post-buckling analysis of composite wing box under loads with torsion-bending coupling

In this paper, a mathematical model for the effect of different torsion-bending ratios on buckling and failure loads under complex operating conditions was proposed, and the buckling, post-buckling and failure behavior and mechanism of carbon fiber composite wing box were studied by combining experiments and simulations. A high-precision computational simulation model was established by considering detailed bolt arrangement information, as well as damage and failure of composite materials. The predicted buckling loads, failure loads, and damage modes are in good agreement with the experimental results. The results show that, under the condition of constant bending moment, as the torque increases, the linear strain of the box section remains consistent, while the shear strain increases significantly, leading to a change in the buckling mode and failure distribution. When the torsion-bending ratio is less than 2.0, the loading form of the structure is greatly affected by the torque. On the contrary, the buckling and failure loads are exponentially close to the pure torsional state. The mathematical model and simulation model in this study can provide an important reference for the structural design and failure mechanism analysis of large composite wing boxes.

Thermo-hygro-mechanical coupling model based on discrete element method to simulate hygrothermal-induced damage of PA6/GF30 material

The present contribution deals with a thermo-hygro-mechanical coupling model based on Discrete Element Method to numerically simulate hygrothermal transfers and their influence on the mechanical behaviour and interfacial damage of composite materials. For application purposes, we consider the context of a 30% glass fibre reinforced polyamide 6 material which is notably used in automotive industry due to its advantageous strength-to-weight ratio. Firstly, a coupled hygrothermal model based on boundary layer theory and the analogy between Fickian mass transfer and heat transfer by conduction is proposed. This is validated in terms of water concentration and water concentration gradient fields, by comparison with Finite Element Method calculations through the simulation of water absorption process in a PA6 material sample. Secondly, a swelling model is proposed to couple hygrothermal transfers to the mechanical behaviour of a material modelled using the hybrid lattice–particle approach. Finally, the hydric swelling-induced interfacial debonding of glass fibre reinforced polyamide 6 material is simulated. Results exhibit the pertinence of the proposed Discrete Element Method based thermo-hygro-mechanical model to reproduce such a process and yields stress and displacement fields with an acceptable level of precision.

Acoustic Emission as a Method for Analyzing Changes and Detecting Damage in Composite Materials During Loading

Thanks to their excellent strength and durability, composite materials are used to manufacture many important structural elements. In the face of their extensive use, it is crucial to seek suitable methods for monitoring damages and locating their origins. The purpose of the article was to verify the possibility of applying the acoustic emissions (AE) method in the detection of damages in the structures of composite materials. The experimental part comprised static tensile tests carried out on various sandwich composites, including simultaneous registration of elastic waves with increasing loads, carried out with the use of an acousticelectrical sensor connected. The signal obtained from the sensor was then further processed and used to draw up diagrams of the AE hits, amplitude, root mean square of the AE source signal (RMS) and duration in the function of time. These diagrams were then applied on their corresponding stretching curves, the obtained charts were analysed. The results obtained point to a conclusion that the acoustic emissions method can be successfully used to detect and locate composite material damages.

Identification of NOL-Ring Composite Materials' Damage Mechanism Based on the STOA-VMD Algorithm.

This research utilized the sooty tern optimization algorithm-variational mode decomposition (STOA-VMD) optimization algorithm to extract the acoustic emission (AE) signal associated with damage in fiber-reinforced composite materials. The effectiveness of this optimization algorithm was validated through a tensile experiment on glass fiber/epoxy NOL-ring specimens. To solve the problems of a high degree of aliasing, high randomness, and a poor robustness of AE data of NOL-ring tensile damage, the signal reconstruction method of optimized variational mode decomposition (VMD) was first used to reconstruct the damage signal and the parameters of VMD were optimized by the sooty tern optimization algorithm. The optimal decomposition mode number K and penalty coefficient α were introduced to improve the accuracy of adaptive decomposition. Second, a typical single damage signal feature was selected to construct the damage signal feature sample set and a recognition algorithm was used to extract the feature of the AE signal of the glass fiber/epoxy NOL-ring breaking experiment to evaluate the effectiveness of the damage mechanism recognition. The results showed that the recognition rates of the algorithm in matrix cracking, fiber fracture, and delamination damage were 94.59%, 94.26%, and 96.45%, respectively. The damage process of the NOL-ring was characterized and the findings indicated that it was highly efficient in the feature extraction and recognition of polymer composite damage signals.

Characterization on fatigue behavior and damage mechanism of C/BMI composites experienced thermal cycling

The multi-directional laminate CCF800H/AC631 bismaleimide composite material was exposed for a long time under the thermal-cycling environment (−60°C ∼+180°C), and the mass loss rate, FTIR spectra, DMA, tensile strength were tested. The fatigue stress level was determined according to the tensile strength and the fatigue performance of the before and after the thermal-cycling environment was tested. Macroscopic visual inspection and ultrasonic C-scan were used to characterize and analyze the fatigue damage of composite materials. The results show that with the increase in the number of thermal cycles, the mass loss of the composite material started with increased rapidly and then basically flat. The C/BMI composites underwent obvious thermal oxygen aging. After thermal-cycling, it would lead to changes in dynamic mechanical properties by a certain degree of post-curing, physical aging, and local interface debonding in composite materials. With the thermal cycles increased the composite material tensile strength first increased slightly and then decreased rapidly. After 300 thermal cycles, the composite materials occurred slightly damaged, and the fatigue life was apparently reduced compared with the original state. The fatigue failure modes of composite materials are mainly fiber fracture and multi-directional laminate delamination. At high stress levels, the stiffness of the specimen after thermal-cycling are lower decrease compared with original specimens, more stress levels would lead to more II stage rate of stiffness decline, and stiffness degradation curve and hysteretic energy recovery curve had enough effect to characterize damage effect of material environment induced by thermal-cycling environment factors.

A Methodology to Automatically Segment 3D Ultrasonic Data Using X-ray Computed Tomography and a Convolutional Neural Network

Ultrasonic non-destructive testing (UT) is a proficient method for detecting damage in composite materials; however, conventional manual testing procedures are time-consuming and labor-intensive. We propose a semi-automated defect segmentation methodology employing a convolutional neural network (CNN) on 3D ultrasonic data, facilitated by the fusion of X-ray computed tomography (XCT) and Phased-Array Ultrasonic Testing (PAUT) data. This approach offers the ability to develop supervised datasets for cases where UT techniques inadequately assess defects and enables the creation of models with genuine defects rather than artificially introduced ones. During the training process, we recommend processing the 3D volumes as a sequence of 2D slices derived from each technique. Our methodology was applied to segment porosity, a common defect in composite materials, for which characteristics such as void size and shape remain immeasurable via UT. Precision, recall, F1 score, and Intersection over Union (IoU) metrics were used in the evaluation. The results of the evaluation show that the following challenges have to be faced for improvement: (i) achieving accurate 3D registration, (ii) discovering suitable similar keypoints for XCT and UT data registration, (iii) differentiating ultrasonic echoes originating from porosity versus those related to noise or microstructural features (interfaces, resin pockets, fibers, etc.), and, (iv) single out defect echoes located near the edges of the component. In fact, an average F1 score of 0.66 and IoU of 0.5 were obtained.