Damage In Composite Materials Research Articles

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.

Study the Mechanical Properties and Fatigue Effect of Multilayer Woven E-Glass/Epoxy Composite under Constant and Variable Loading

The present work studies the effect of adding woven E-glass fibers [0°/90°], 16 layers with 50% weight fraction to pure epoxy (matrix) on fatigue behavior under constant and variable loadings. The CNC water jet cutting machine was used to cut the composite samples with five fiber direction angles, such as (0°, 5°, 15°, 30°, and 45°). The tensile test was used to determine the composite material’s mechanical properties. The results showed that the composite material with 5° of fiber direction had the highest ultimate tensile stress, i.e., 353 MPa, and the highest Young's Modulus, i.e., 11940 MPa, compared to other samples with angles of (0°, 15°, 30°, 45°) of fiber direction. The sample with 5° angle was adopted in constant and variable fatigue loading tests. The fatigue test results under constant loading showed a 24.8 times fatigue strength improvement for composite material at 107 cycles compared to pure epoxy. The fatigue test under variable loading was conducted with two types of sequence loading program tests at a constant number of cycles at each stress level: high-low sequence loading with stress of 170-130 MPa with 10,000 and 20,000 cycles for each stress level and low-high sequence loading with stress 130-170 MPa with 10,000 and 20,000 cycle for each stress level loading and so on to failure. The results showed that the fatigue life under high-low sequence loading for both 10,000 and 20,000 cycles was less than that of the low-high sequence loading. Also, the results showed that Miner's rules were safe to calculate the damage of composite material used in this work where the damage was above one (D > 1).

On the Cover: Comparing Methods to Detect the Formation of Damage in Composite Materials

On the Cover: Comparing Methods to Detect the Formation of Damage in Composite Materials

Probabilistic Risk Assessment of Accidental Damage to Civil Aircraft Composite Structures

<div>In view of the structural accidental events in the ongoing airworthiness stage of civil aircraft, it is necessary to conduct a risk assessment to ensure that the risk level is within an acceptable range. However, the existing models of risk assessment have not effectively dealt with the risk of accidental structural damage due to random failure. This article focuses on probabilistic risk assessment using the Transport Airplane Risk Assessment Methodology (TARAM) of accidental structural damage of civil aircraft. Based on the TARAM and probability reliability integral, a refined failure frequency probability calculation model is established to elaborate on composite structure failure frequency. A case study is analyzed for the outer wing plane of an aircraft having impact damage of composite materials. Finally, results of the risk assessment without correction and risk assessment with correction are presented for detailed visual inspection and general visual inspection.</div>

A Continuum Damage-Based Anisotropic Hyperelastic Fatigue Model for Short Glass Fiber Reinforced Polyamide 66

A phenomenological 3D anisotropic nonlinear fatigue damage model has been developed for a short glass fiber-reinforced polyamide. The model is formulated within the framework of continuum damage mechanics and is based on a proposed anisotropic hyperelastic strain energy function. The proposed model accounts for the effects of fiber content and nonlinear material behavior. The mechanical behavior of polyamide reinforced with 20% and 30% wt short glass fiber has been experimentally investigated under quasi-static and fatigue loading. Fatigue tests under bending loading are carried out on rectangular specimens cut in the parallel and perpendicular direction to the mold flow direction. The proposed fatigue damage model allows predicting the fatigue damage of composite materials reinforced with short fiberglass, considering fiber orientation and fiber content. The model is used to predict the damage evolution and the number of cycles to failure, and good agreement between predicted values and experimental data is observed.

Self-learning locally-optimal hypertuning using maximum entropy, and comparison of machine learning approaches for estimating fatigue life in composite materials of the aerospace industry

Applications of Structural Health Monitoring (SHM) combined with Machine Learning (ML) techniques enhance real-time performance tracking and increase structural integrity awareness of civil, aerospace and automotive infrastructures. This SHM-ML synergy has gained popularity in the last years thanks to the anticipation of maintenance provided by arising ML algorithms and their ability of handling large quantities of data and considering their influence in the problem.In this paper we develop a novel ML nearest-neighbors-alike algorithm based on the principle of maximum entropy to predict fatigue damage (Palmgren–Miner index) in composite materials by processing the signals of Lamb Waves – a non-destructive SHM technique – with other meaningful features such as layup parameters and stiffness matrices calculated from the Classical Laminate Theory (CLT). The full data analysis cycle is applied to a dataset of delamination experiments in composites. The predictions achieve a good level of accuracy, similar to other ML algorithms, e.g. Neural Networks or Gradient-Boosted Trees, and computation times are of the same order of magnitude.The key advantages of our proposal are: (1) The automatic determination of all the parameters involved in the prediction, so no hyperparameters have to be set beforehand, which saves time devoted to hypertuning the model and also represents an advantage for autonomous, self-supervised SHM. (2) No training is required, which, in an online learning context where streams of data are fed continuously to the model, avoids repeated training—essential for reliable real-time, continuous monitoring.

Probability-Based Diagnostic Imaging of Fatigue Damage in Carbon Fiber Composites Using Sparse Representation of Lamb Waves

Carbon fiber composites are commonly used in aerospace and other fields due to their excellent properties, and fatigue damage will occur in the process of service. Damage imaging can be performed using damage probability imaging methods to obtain the fatigue damage condition of carbon fiber composites. At present, the damage factor commonly used in the damage probability imaging algorithm has low contrast and poor anti-noise performance, which leads to artifacts in the imaging and misjudgment of the damaged area. Therefore, this paper proposes a fatigue damage probability imaging method for carbon fiber composite materials based on the sparse representation of Lamb wave signals. Based on constructing the Lamb wave dictionary, a fast block sparse Bayesian learning algorithm is used to represent the Lamb wave signals sparsely, and the definition of Lamb wave sparse representing the damage factor calculates the damage probability of the monitoring area and then images the fatigue damage of the carbon fiber composite materials. The imaging research was carried out using the fatigue monitoring experiment data of NASA’s carbon fiber composite materials. The results show that the proposed damage factor can clearly distinguish the damaged area from the undamaged area and has strong noise immunity. Compared with the energy damage factor and the cross-correlation damage factor, the error percentages are reduced by at least 58.63%, 28.11%, and 8.43% for signal-to-noise ratios of 6 dB, 3 dB, and 0.1 dB, respectively, after adding noise to the signal. The results can more accurately reflect the real location and area of fatigue damage in carbon fiber composites.

Automatic defect depth estimation for ultrasonic testing in carbon fiber reinforced composites using deep learning

Ultrasonic testing (UT) is commonly used to inspect the geometric shape of internal damage in composite materials and the test results need to be interpreted by trained experts. In this work, an automatic signal classification method based on deep learning is proposed for depth estimation of the detects introduced by low-velocity impact (LVI) in carbon fiber reinforced plastics (CFRPs). Three kinds of neural networks, LSTM, CNN, and CNN-LSTM are used to analyze the attributes with different depths. Then, trained models are applied to identify the depth information of impact damage. The results show that the CNN-LSTM model is a more accurate in-depth classification for LVI defects in CFRP based on A-scan signals than the other two structures.

A computational study of AE signal due to fiber break and fiber matrix debonding in model composite

Acoustic emission monitoring is a useful method to deal with detection and identification of damage in composite materials during mechanical tests. From an experimental point of view, it is not straightforward to establish a quantitative link between the damage sources and AE signals. Modeling and numerical simulations appear as a promising way to reach this objective. In this study, Finite Elements Modeling (FEM) was used to simulate AE signals due to fiber break and fiber/matrix debonding in a model carbon fiber composite. A specimen made of a single carbon fiber and epoxy matrix was used to experimentally validate the simulated fiber break AE signals. The objective was to compare the AE signals from a validated fiber break simulation (Fig. 1a) to the AE signals obtained from fiber/matrix debonding (with several models) and fiber break obtained in several media and to discuss the capability to detect and identify each source (Fig. 1b). The influence of the type of sensor, specimen geometry and AE source location were also studied. The proposed model was extended to identify the main parameters that influence the acoustic emission signatures. These validated results open the way to combine the experimental and the simulated data to generate a library in order to identify real-time damage mechanisms. Fig. 1: (a) Amplitude and roll-off frequency vs. source/sensor distance for experimental signals and numerical signals. (b)Evolution of Partial Power PP1 [0–125 kHz] as a function of amplitude obtained with a nano 30 sensor for several sources. Fiber break and debonding (L = 20 μm or 100 μm). EWGAE 35, Ljubljana, Slovenia, 13th – 16th Sep. www.ewg

Investigating the impact of different machinability processes and fibre architecture on the bearing performance of pin-loaded textile structural composites for automotive applications: Experimental and finite element analysis

The present study aims to investigate the effect of different machinability processes such as drilling, abrasive water jet machining (AWJM), and laser beam machining (LBM) along with different fibre architectures on the behavior of machined hole, bearing strength (joint performance), and failure mechanism of different textile fibre-reinforced structural composites (TFRSC) fabricated using glass, basalt, and sisal fibres suitable for automotive applications. Different structural preforms such as chopped fibre, unidirectional (UD), bidirectional (2D), and three-dimensional (3D) woven orthogonal structures were developed and subsequently converted to their respective composite forms using the vacuum-assisted resin transfer molding (VARTM) process. Morphological damage evaluation and fractography of the developed composite materials were carried out by optical microscopic analysis. The experimental observations revealed that the basalt fibre reinforced textile structural composite (BFRTSC) specimens exhibited the highest bearing strength compared to glass fibre reinforced textile structural composites (GFRTSC) and sisal fibre reinforced textile structural composites (SFRTSC) for all the processes. The bearing strength of composites was in the order of 3D > chopped > 2D > UD, respectively. Additionally, a novel methodical mechanics-based approach was introduced to develop the finite element modeling (FEM) mesoscale model using SOLIDWORKS to study the bearing response of pin-loaded TFRSC. The well-established 3D Hashin's failure and Puck's failure models were used to predict the woven-based composite material damage modes, and it was implemented through a user subroutine along with LS-DYNA. The bearing response predicted by the FEM simulation was found in a good agreement with the experimental observations.

Lightning damage of composite material driven by multi-physics coupling

Lightning damage of carbon fiber/epoxy composite material results from multi-physics coupling effects including electricity, magnetism, heat and force, etc. Physical behaviors of lightning magnetic fluid and structural response of composite laminate are numerical simulated based on Magneto Hydro Dynamics (MHD) and thermo-electric coupling theory, respectively. The radial basis function (RBF) and high precision dynamic mesh technology are proposed to exchange data of multi-physics and structural response at the interface. Temperature dependent material parameters and latent heat of phase change are considered to reveal lightning damage mechanism of composite laminate. Thermal decomposition, ablation area and depth of composite laminate are predicted by numerical approach and verified through lightning test.

Failure Behavior of Composite Bolted Joints: Review

The fiber-reinforced polymeric (FRP) composite can be joined by adhesive bonding or mechanically fastening the composite element. Carbon fiber-reinforced polymers (CFRP) and glass fiber-reinforced polymers (GFRP) are the most common types of polymers. Mechanically fastened or bolted joints do not need treatments, and the surface temperature and other environmental effects such as humidity do not affect its strength. In this research, the previous research that describes the behavior of the composite bolted joint (CBJ) is presented and summarized including the modes of failure of the CBJ, and the experimental works that explain the bearing failure (BF) of the CBJ. In addition, the effects of bolt clamping force (BCF) and techniques that are used to simulate the progressive failure and damage in composite materials and finite element simulation works are surveyed.

On the Use of Digital Image Correlation to Assess the Damage Behavior of Composite Coupons Under Compression

AbstractA strong increase in the use of composite materials has been observed in all the industrial fields, related to manufacturing of lightweight load‐bearing components. However, despite the excellent mechanical properties that characterize such innovative materials, uncertainties still exist on their complex failure mechanisms. Hence, the use of non‐destructive techniques and experimental tests is needed to increase the understanding of composite materials damage evolution. This paper deals with the use of the Digital Image Correlation (DIC) technique to investigate the failure mechanisms of delaminated composite panels under compressive loading conditions, accounting for the complex phenomena related to unstable delamination growth due to buckling snap‐through. The panels have been produced by tuning manufacturing parameters to test their influence on toughening mechanisms such as fiber bridging. The main aim is to understand how to improve laminate out‐of‐plane performance and delay the delamination growth phenomenon. The obtained data in terms of strain and displacements distributions has been used to assess the phenomenon of delamination propagation related to local delamination and global panels’ instabilities.

Influence of Sensor Position and Low-Frequency Modal Shape on the Sensitivity of Vibro-Acoustic Modulation for Impact Damage Detection in Composite Materials

Very sensitive structural health monitoring methods are needed to detect barely visible impact damage in composite materials. Based on extracting non-linear modulated components from the frequency response of the damaged system, vibro-acoustic modulation (VAM) has shown to be effective in identifying the presence of damage at its early stage. A decisive role in the success of this technique is played by the choice of the high-frequency probe and the low-frequency pump sinusoidal signals that simultaneously excites the system. This study explores how the position of the sensing transducer, with respect to the modal shape of the pump excitation, may influence the sensitivity of the VAM technique for impact damage detection in composite laminates. This aspect has been scarcely investigated in previous research works, as other studies have focused more on the role of the probe frequency. Here, VAM tests were performed on a composite beam by using a frequency-swept pump vibration simultaneously with a high frequency probe excitation. The results of the experimental tests indicate that the VAM technique is capable of clearly revealing the presence of impact damage only when the sensor is placed on appropriate locations, which are directly related to the shape of the deformation activated by the applied excitation. These results suggest the adoption of low frequency excitations that activate multiple modal shapes to improve the effectiveness and reliability of VAM approaches.