Search Results (170)

Carbon-fibre reinforced polymer (CFRP) laminates are susceptible to both static and fatigue-driven delamination. Predicting this type of failure in curved composite structures, often referred to as delamination by unfolding, remains a critical challenge. This work presents the development of a Virtual Crack Closure Technique (VCCT)-based computational method for simulating fatigue-driven delamination propagation under non-constant mixed-mode conditions. The fatigue delamination growth model follows a phenomenological approach based on a Paris–Erdogan-based power-law relationship, where the delamination propagation rate depends on the strain energy release rate. This methodology has been implemented as a user-defined subroutine, UMIXMODEFATIGUE, for Abaqus, integrating the effects of load ratio and mode mixity conditions while leveraging the mode separation provided by VCCT. The proposed approach is validated against an experimental case involving a four-point bending test applied to an L-shaped CFRP curved beam specimen with a unidirectional layup. Unlike the existing standard configuration, the proposed test campaign introduces a non-adhesive Teflon foil insert at the bend, placed within the midplane layers to act as a delamination initiator, representing a manufacturing defect. In addition to the testing machine, digital image correlation (DIC) is used to monitor delamination length. The simulation method developed accurately predicts fatigue delamination propagation under varying mode mixity at the delamination front. By improving delamination modelling in composites, this approach supports timely maintenance and helps prevent fatigue failures. Additionally, it deepens the understanding of how the mode mixity influences the delamination propagation process. Full article

X-ray micro-computed tomography (Micro-CT) is an advanced technique capable of non-destructive detection of internal defects in materials. Fiber-reinforced polymer (FRP) laminates are prone to forming defects such as pores during the manufacturing process, which significantly affect their mechanical properties. In this study, Micro-CT technology was employed to conduct non-destructive testing on carbon fiber (CFRP), glass fiber (GFRP) and carbon/glass hybrid (C/G) laminates. Combined with the U-Net++ deep learning model, precise segmentation and three-dimensional reconstruction of pores were achieved. A systematic quantitative analysis was carried out on the distribution, size, volume and porosity of pores in six specimens with two layup angles (0/90 and ±45). The research results show that the pores in CFRP are mainly dispersed micro-pores and are relatively evenly distributed; the porosity of GFRP is the highest, and large interlaminar pores are prone to forming. The porosity fluctuates sharply in the thickness direction, revealing that the interlaminar interface is a defect-sensitive area. This provides a reliable quantitative basis and theoretical support for optimization and defect assessment. Full article

This paper compares three nondestructive methods used to detect and locate defects such as delaminations or voids in externally bonded fiber reinforced polymer (FRP) concrete structures: infrared thermography, ground-penetrating radar, and measurement of acoustic wave velocity. One of the main goals was to check whether it was possible to distinguish overlapping defects. For this purpose, eight concrete samples were made with a bonded carbon fiber reinforced polymer (CFRP) strip with the following dimensions 100 × 100 × 500 mm. Two samples had no defects, four had single defects varying in location (at the edge of the strip or in the centre) simulating delamination or voids in the concrete cover, and the remaining samples had overlapping defects. Both infrared thermography and acoustic wave velocity measurement methods allow the detection of defects/voids in the adhesive layer and a concrete defect (void in the concrete cover). However, ground penetration failed to detect defects in the adhesive layer. Only infrared thermography allows for the differentiation of overlapping defects. On the basis of the conducted research, the methodology, differences, advantages, and limitations of each method were described, along with recommendations based on the authors’ experience. Full article

CFRP is extensively utilized in the manufacturing of aerospace equipment owing to its distinctive properties, and hole-making processing continues to be the predominant processing method for this material. However, due to the anisotropy of CFRP, in its processing process, processing damage appears easily, such as stratification, fiber tearing, burrs, etc. These damages will seriously affect the performance of CFRP components in the service process. This work employs acoustic emission (AE) and infrared thermography (IT) techniques to analyze the characteristics of AE signals and temperature signals generated during the CFRP drilling process. Fast Fourier transform (FFT) and short-time Fourier transform (STFT) are used to process the collected AE signals. And in combination with the actual damage morphology, the material removal behavior during the drilling process and the AE signal characteristics corresponding to processing defects are studied. The results show that the time-frequency graph and root mean square (RMS) curve of the AE signal can accurately distinguish the different stages of the drilling process. Through the analysis of the frequency domain characteristics of the AE signal, the specific frequency range of the damage mode of the CFRP composite material during drilling is determined. This paper aims to demonstrate the feasibility of real-time monitoring of the drilling process. By analyzing the relationship between the RMS values of acoustic emission signals and hole surface topography under different drilling parameters, it provides a new approach for the research on online monitoring of CFRP drilling damage and improvement of CFRP machining quality. Full article

To replicate delaminations at the coupon and substructural scales, simulated defects are often introduced into test specimens; therefore, understanding their behaviour within the laminate is essential. Full-field imaging is employed to investigate the effects of artificial defects in Carbon Fibre-Reinforced Polymer (CFRP) composites. Centre Crack Ply (CCP) specimens are used to evaluate the Mode II fracture toughness of laminated composites from a simple tensile test. Two batches of specimens are manufactured using IM7/8552. Artificial defects are introduced using a steel film insert of 5 µm thickness. For the first type of samples, the inserts were coated with Frekote release agent, while for the second type, the steel inserts were incorporated into the laminate without coating. Additionally, a third batch of specimens with a [0₄, 90]s layup is manufactured. Thermoelastic Stress Analysis (TSA) and Digital Image Correlation (DIC) are employed to obtain full-field temperature and displacement data from the tested samples. The inclusion of 90-degree plies enhances thermal contrast exploiting, their anisotropic mechanical and thermal properties. First, the specimens are tested under monotonic loading to failure, with DIC used to capture strain distributions at damage initiation and failure. In addition, acoustic emission is employed to evaluate damage initiation. Load drops provide an indirect evaluation of fracture toughness. Results show that full-field imaging is capable of establishing how the release agent and the layup configuration influence damage initiation and propagation. The non-adiabatic thermoelastic response is shown to be effective in observing subsurface damage. Finally, a novel approach to evaluate fracture toughness from the temperature increase at the failure event is proposed. Full article

A Mode I Interlaminar Fracture Toughness test of carbon fiber-reinforced polymer laminates requires a double cantilever beam (DCB) specimen with a pre-implanted non-adhesive insert at the mid-plane to initiate delamination. However, the insert’s quality and placement within the DCB specimen can be problematic, necessitating non-destructive testing methods. In this study, active thermography is employed to inspect potential defects around the Teflon insert in the DCB specimens. Both uniform and non-uniform heating methods have been applied, and thermal images were analyzed to obtain quantitative information, such as the insert’s location and non-contact area. TSR-enhanced images were obtained using two variations in the classical thermographic signal reconstruction. The analyzed results confirmed the presence of non-contact areas in the DCB structures composed of both 22-layer and 24-layer CFRP prepregs. These areas may be attributed to residual air gaps formed during the hot-press molding of the DCB structures. Full article

Detecting and quantifying barely visible impact damage (BVID) in carbon fiber-reinforced polymer (CFRP) materials is a key challenge in maintaining the safety and reliability of composite structures. This study presents the application of low-power vibrothermography to identify and quantify such defects. Using a long-wave infrared (LWIR) camera, thermal data were captured from the CFRP specimens that inhibit BVID. How image processing, specifically principal component analysis (PCA) and sparse principal component analysis (SPCA), can enhance thermal contrast and improve the accuracy of defect size is also explored. By combining low-energy excitation with advanced data analysis, this research aims to develop a more accessible and reliable approach to non-destructive testing (NDT) for composite materials. Full article

This paper reviews recent advances in machine learning (ML) algorithms to improve the postprocessing and interpretation of thermographic data in non-destructive testing (NDT). While traditional NDT methods (e.g., visual inspection, ultrasonic testing) each have their own advantages and limitations, thermographic techniques (e.g., pulsed thermography, laser thermography) have become valuable complementary tools, particularly in inspecting advanced materials such as carbon fiber-reinforced polymers (CFRPs) and superalloys. These techniques generate large volumes of thermal data, which can be challenging to analyze efficiently and accurately. This review focuses on how ML can accelerate defect detection and automated classification in thermographic NDT. We summarize currently popular algorithms and analyze the limitations of existing workflows. Furthermore, this structured analysis provides an in-depth understanding of how artificial intelligence can assist in processing NDT data, with the potential to enable more accurate defect detection and characterization in industrial applications. Full article

Carbon fiber reinforced polymer (CFRP) composites are prone to developing localized material loss defects during long-term service, which can severely degrade their mechanical properties and structural reliability. To address this issue, this study proposes a multi-sensor synchronous monitoring method combining embedded fiber Bragg grating (FBG) sensors and surface-mounted electrical resistance strain gauges. First, finite element simulations based on the three-dimensional Hashin damage criterion were performed to simulate the damage initiation and propagation processes in CFRP laminates, revealing the complete damage evolution mechanism from initial defect formation to progressive failure. The simulations were also used to determine the optimal sensor placement strategy. Subsequently, tensile test specimens with prefabricated defects were prepared in accordance with ASTM D3039, and multi-sensor monitoring techniques were employed to capture multi-parameter, dynamic data throughout the damage evolution process. The experimental results indicate that embedded FBG sensors and surface-mounted strain gauges can effectively monitor localized material loss defects within composite laminate structures. Strain gauge measurements showed uniform strain distribution at all measuring points in intact specimens (with deviations less than 5%). In contrast, in defective specimens, strain values at measurement points near the notch edge were significantly higher than those in regions farther from the notch, indicating that the prefabricated defect disrupted fiber continuity and induced stress redistribution. The combined use of surface-mounted strain gauges and embedded FBG sensors was demonstrated to accurately and reliably track the damage evolution behavior of defective CFRP laminates. Full article

This study explores the application of low-power vibrothermography (LVT) for detecting barely visible impact damage (BVID) in carbon fibre-reinforced polymer (CFRP) laminates. Composite specimens with varying impact energies (2.5–20 J) were excited using a single piezoelectric transducer with a nominal centre frequency of 28 kHz, operated at a fixed excitation frequency of 28 kHz. Thermal data were captured using an infrared camera. To enhance defect visibility and suppress background noise, the raw thermal sequences were processed using principal component analysis (PCA) and robust principal component analysis (RPCA). In LVT, RPCA and PCA provided comparable signal-to-noise ratios (SNR), with no consistent advantage for either method across all cases. In contrast, for pulsed thermography (PT) data, RPCA consistently resulted in higher SNR values, except for one sample. The LVT results were further validated by comparison with PT and phased array ultrasonic testing (PAUT) data to confirm the location and shape of detected damage. These findings demonstrate that LVT, when combined with PCA or RPCA, offers a reliable method for identifying BVID and can support safer, more efficient structural health monitoring of composite materials. Full article

The thermal stability of carbon fiber-reinforced plastic (CFRP) materials is constrained by the low thermal conductivity of its polymer matrix, resulting in inefficient heat dissipation, local overheating, and accelerated degradation during thermal loads. To overcome these limitations, composite materials can be modified with buckypapers—thin, densely interconnected layers of carbon nanotubes (CNTs). In this study, sixteen 8552/IM7 prepreg plies were processed with up to nine buckypapers and strategically placed at various positions. The resulting nanocomposites were evaluated for manufacturability, material properties, and thermal resistance. The findings reveal that prepreg plies provide only limited matrix material for buckypaper infiltration. Nonetheless, up to five buckypapers, corresponding to 8 wt.% CNTs, can be incorporated into the material without inducing matrix depletion defects. This integration significantly enhances the material’s thermal properties while maintaining its mechanical integrity. The nanotubes embedded in the matrix achieve an effective thermal conductivity of up to 7 W/(m·K) based on theoretical modeling. As a result, under one-sided thermal irradiation at 50 kW/m², thermo-induced damage and strength loss can be delayed by up to 20%. Therefore, thermal resistance is primarily determined by the nanotube concentration, whereas the arrangement of the buckypapers affects the material quality. Since this innovative approach enables the targeted integration of high particle fractions, it offers substantial potential for improving the safety and reliability of CFRP under thermal stress. Full article

Fuzz defects are prevalent surface imperfections in carbon fiber-reinforced polymer (CFRP) prepregs. Current manual inspection methods or conventional neural network-based approaches face significant limitations in achieving standardized and accurate severity assessment of such defects. In this article, a methodology comprising three key technical innovations is proposed: First, an adaptive thresholding algorithm is implemented, utilizing local average grayscale values to accurately identify fuzz defect pixels. Second, a grayscale histogram analysis is performed on the identified defect regions to quantify severity levels, effectively mitigating the influence of substrate material variations and illumination conditions on assessment accuracy. Third, a quantitative formula is defined based on the detection boxes drawn by the neural network object detection model and the effective area of defects to evaluate the severity of fuzz defects. Experimental validation shows 90% consistency with practical manual assessment in defect severity ranking tasks, proving its industrial applicability. Full article

During the machining of unidirectional carbon fiber-reinforced polymers (UD-CFRPs), their anisotropic characteristics and the complex cutting conditions often lead to defects such as delamination, burrs, and surface/subsurface damage. This study systematically investigates the effects of different fiber orientation angles (0°, 45°, 90°, and 135°) on cutting force, chip formation, stress distribution, and damage characteristics using a coupled macro–micro finite element model. The model successfully captures key microscopic failure mechanisms, such as fiber breakage, resin cracking, and fiber–matrix interface debonding, by integrating the anisotropic mechanical properties and heterogeneous microstructure of UD-CFRPs, thereby more realistically replicating the actual machining process. The cutting speed is kept constant at 480 mm/s. Experimental validation using T700S/J-133 laminates (with a 70% fiber volume fraction) shows that, on a macro scale, the cutting force varies non-monotonically with the fiber orientation angle, following the order of 0° < 45° < 135° < 90°. The experimental values are 24.8 N/mm < 35.8 N/mm < 36.4 N/mm < 44.1 N/mm, and the simulation values are 22.9 N/mm < 33.2 N/mm < 32.7 N/mm < 42.6 N/mm. The maximum values occur at 90° (44.1 N/mm, 42.6 N/mm), while the minimum values occur at 0° (24.8 N/mm, 22.9 N/mm). The chip morphology significantly changes with fiber orientation: 0° produces strip-shaped chips, 45° forms block-shaped chips, 90° results in particle-shaped chips, and 135° produces fragmented chips. On a micro scale, the microscopic morphology of the chips and the surface damage characteristics also exhibit gradient variations consistent with the experimental results. The developed model demonstrates high accuracy in predicting damage mechanisms and material removal behavior, providing a theoretical basis for optimizing CFRP machining parameters. Full article

Carbon fibre reinforced composite (CFRP) laminates are widely used in high-tech industries. However, their assembly often requires a drilling process that can create defects. Therefore, studies on the drill tip angle have sought to minimize the surface area affected by these defects and improve the internal hole quality. In this work, drilling was carried out under dry conditions at a constant cutting speed for four different feed rates in the epoxy–carbon-based thermosetting laminate (EPX-C). Two carbide drills with point angles of 118° and 140° were used. The results showed the occurrence of chipping-type delaminations on both the hole entry and exit surfaces, with the latter being more severely affected. The delamination factor values obtained indicated that the 118° drill performed better than the 140° drill. The results were also compared with those obtained in a previous study using drills with angles of 60° and 130°. Although the values were higher, they followed the same trend of reduction with increasing feed. In terms of surface finish, the average roughness (Ra) values obtained with the 140° drill were better at the lowest feed rate. Full article