Damage In Composite Materials Research Articles

Lightning ablation damage and residual bending performances of scarf-repaired composite laminates with copper mesh protection

The copper mesh provides effective lightning strike protection for composite structures, while its efficiency can be compromised in scarf-repaired composite structures. In this study, after verified by test results, a numerical analysis procedure was employed to predict the lightning ablation damage and residual bending performances of scarf-repaired composite laminates with copper mesh protection. Initially, the ablation damage and bending damage mechanisms of good repaired and poor repaired composite laminates were analyzed. Then, the impact of lightning strike attachment position and repair design parameters including overlap length and thickness of adhesive layer on the lighting protection performances was investigated. The results indicate that ablation damage of composite and adhesive materials near the repair area significantly compromises structural integrity and reduces the bending strength. The predominant bending failure mode of scarf-repaired woven composite laminates after lightning strike is fiber failure in warp direction, accompanied by small areas of matrix failure and delamination failure. The reestablishment level of the electrical path varies with overlap length and thickness of adhesive layer, subsequently affecting the ablation damage area and depth. This study can provide a reference for the design and process control of scarf-repair in copper mesh protected composite structures.

Prediction of Ductile Damage in Composite Material Used in Type IV Hydrogen Tanks by Artificial Neural Network and Machine Learning with Finite Element Modeling Approach

This study investigates the degradation process of composite materials used in high‐pressure hydrogen storage vessels by employing advanced computational techniques. A recurrent neural network, specifically a bidirectional long short‐term memory (Bi‐LSTM) network, is utilized to predict the temporal evolution of ductile damage. The key degradation features are extracted from finite element modeling (FEM) computations using group method of data handling algorithms and treated as time‐series data. Results demonstrate that the Bi‐LSTM network can accurately undergo both elastic and plastic behaviors of the composite under tensile strength. Additionally, traditional machine learning (ML) algorithms such as extreme gradient boosting and random forest are employed to forecast strain degradation, showing promising results. This hybrid approach combining FEM, ML, and deep learning provides a comprehensive method for predicting the degradation of composite materials, offering significant potential for optimizing the design and durability of hydrogen storage vessels.

Composite delamination damage location based on non-orthogonal air-coupled ultrasonic guided waves and Artifact-Reduction damage index

This study focuses on the localization of delamination damage in composite materials. Due to the attenuation of the air-coupled ultrasound at the gas–solid coupling interface, the signal-to-noise ratio of the air-coupled ultrasonic signal is low, which results in low accuracy of damage detection. This paper proposes a method to locate delamination damage in composite materials. This method uses non-orthogonal Air-coupled Ultrasonic Guided Waves for B-scan; an artifact-reduction damage index (ARDI) is designed with the proportion of the scan paths containing damage information to the entire scan paths. These two exquisite designs improve detection accuracy. Finally, the probability of damage occurrence is calculated, and the detection of the damage location is achieved, with the positioning error of the damage being less than 1.5%.

Numerical study of composite structures behavior under ballistic impacts

The aim of this paper is to introduce a finite element study of the behavior of composite plates subjected to ballistic impacts. The work studies different configurations of laminated composite structures, with and without reinforcements, subjected to high-speed projectile impacts (400-1000 m/s). The investigation focuses on the response of different composite materials' stacking sequences, as well as the impact of epoxy carbon reinforced with carbon nanotubes and the behavior of hybrid sandwich structures made of Kevlar epoxy and glass epoxy. The purpose is to develop a strategy of composite material damage modelling, identify the effects of multi material hybridization, and the influence of reinforcement by carbon nanotubes, on the structures' resistance to ballistic impacts. Numerical simulation provides a detailed description of plate damage. Results show that composite plates with 2% carbon nanotubes (CNT) displays a better impact energy absorption compared to 1% CNT and plates without CNT. The study also revealed the hybridization sequences offering the greatest energy absorption.

In-situ detection of delamination reinitiation in carbon fiber reinforced polymers post barely visible impact damage

In this study, advancements are presented in the in-situ detection of delamination reinitiation from Barely Visible Impact Damage (BVID) in composite materials, utilizing enhancements in Digital Image Correlation (DIC) techniques during a Compression After Impact (CAI) test. The study measured strain fields in the longitudinal, transverse, and shear directions, focusing specifically on the point of highest out-of-plane displacement to identify the onset of delamination propagation from BVID sites generated at different impact energy levels. By correlating the measured strains with the peak out-of-plane displacement, a unique determination of onset damage reinitiation associated with BVID during CAI testing was achieved. This method introduces a refined in-situ assessment technique for structural integrity, allowing for the early detection of critical damage propagation in composite materials.

Nonlinear finite element damage analysis of laminated shells by Carrera Unified Formulation

The paper presents a comprehensive study on the nonlinear finite element analysis of laminated shells, employing the Carrera Unified Formulation (CUF) in conjunction with a progressive damage model developed by Tita et al. (2008). This research focuses on accurately predicting the behavior of laminated composite structures under progressive damage. The model accounts for both polymer matrix and fiber failures, and the numerical implementation and simulations are performed using MATLAB. The study validates the proposed framework by comparing the numerical results with experimental data, demonstrating a good agreement and thus confirming the model’s capability to capture the nonlinear behavior of laminated shells during damage progression. The research also highlights the importance of the CUF in providing a versatile approach to finite element analysis, allowing for different families of elements and, thickness expansions and use of failure criteria that consider out of plane stresses, which can be particularly useful for a more detailed and physically consistent analysis of damage in composite materials.

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.

Data-driven Parameterizable Generative Adversarial Networks for Synthetic Data Augmentation of Guided Ultrasonic Wave Sensor Signals

Detecting and characterizing hidden damages in composite materials like Fibre-Metal Laminates (FML) remains a challenge. Guided Ultrasonic Waves (GUW) or X-ray imaging are commonly used to detect these damages, but their interpretation remains limited. Data-driven predictor models can detect damages in structures using GUW time-dependent signals, but the training data lacks variance, quality, and sufficient coverage of the hyper-parameter space. Synthetic data augmentation is often the only solution to create robust and generalized damage predictor models. Synthetic sensor data can be generated using model-based, model-assisted, or model-free methods. However, computed GUW signals show poor reality conformance due to too much constraints and simplifications. Recent developments in Generative Adversarial (Neural) Networks (GAN), commonly driven by a random generation process [1], include deterministic style vectors to generate specific signal data, determining constraints such as damage size, position, transducer positions, material and environmental properties. These new architectures aim to reduce the impact of environmental changes on data-driven damage predictor models by using parameterizable synthetic data generation. We will elaborate these new architectures and discuss the possibilities and challenges of using such GAN models for data generation of US signals in GUW-based SHM applications, as originally proposed in [2]. We will have a focus on low-quality real measuring data used for training and composite materials (e.g., FML) with hidden damages, and show some preliminary results. [1] Karras et al. "A style-based generator architecture for generative adversarial networks." Proceedings of the IEEE/CVF 2019 [2] Virupakshappa et al. "Using Generative Adversarial Networks to Generate Ultrasonic Signals." 2020 IEEE International Ultrasonics Symposium (IUS). IEEE, 2020

A transfer learning approach for data-driven localization of damage areas in plate-like structures of CFRP materials

The application of machine learning techniques based on Lamb waves for structural health monitoring of composite material damage has been widely adopted. However, this method often struggles to acquire sufficient labeled historical Lamb wave signals for training machine learning models. This issue can be addressed through the implementation of transfer learning. This paper proposes a data-driven transfer learning model for locating coordinates of damage areas in plate-like structures with complex geometric features such as rivets and grooves. Based on the differences in conditional distributions in various structural health monitoring scenarios, a Cross-Component Regression for Damage Localization Model (CRDM) is introduced for damage localization across components. Due to the limited availability of labeled target data, CRDM aims to preserve global properties of the target data-dominated conditional distribution, besides considering the predictive accuracy of individual samples. Therefore, a hybrid loss function is constructed by combining Mean Squared Error (MSE) and Conditional Embedding Operator Difference (CEOD). Subsequently, the target model is fine-tuned based on this loss function. Experimental validations are conducted on finite element models and real datasets, confirming the effectiveness of the CRDM model in damage localization for plate-like structures.

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.