Carbon Fiber Reinforced Plastic Research Articles

Multiscale numerical scheme on shear failure feature of 3DOWC/Cs bar incorporating microlevel uncertainties and coupled constitutive behavior

In contrast to prevalently utilized carbon fiber reinforced plastics (CFRP), the carbon fiber reinforced carbon matrix composites (C/Cs) frequently present significantly divergent mechanics features. The current investigation dedicates to a robust multiscale finite element strategy on shear failure characteristics of three-dimensional orthogonal woven C/Cs (3DOWC/Cs) bolt bar together with relevant experimental measurement for validation, while the off-axial orientation sensibility of structural load-bearing performance and damage mechanism are systematically evaluated. In view of the complicated internal fabric architecture, the predictions are received via hierarchical numerical simulation at micro-, meso-, as well as macroscales. The random distributions of voids and fibers are incorporated to precisely capture the material properties, meanwhile the constitutive laws which embed the combining of Hashin and Puck criterions judging for yarn initial damage, multilinear rule for carbon matrix, and trilinear cohesive zone model (CZM) for interface, are implemented to render the coupling impacts of material behaviors. Besides, the extracted global load-displacement curves and local progressive failure morphologies from macroscale simulation are separately compared with experimental acquisitions to verify the accuracy of proposed modelling and to expose the damage mechanism of specimens with various off-axis angles. The contribution of this research lies in accurate quantification for microlevel uncertainties via modified stochastic algorithms, and applicable constitutive frame severally matching each material component to portray the distinctive mechanical particularities of 3DOWC/Cs.i

Machine learning constructs the microstructure and mechanical properties that accelerate the development of CFRP pyrolysis for carbon-fiber recycling

The increasing use of carbon-fiber-reinforced plastic (CFRP) has led to its post-end-of-life recycling becoming a research focus. Herein, we studied the macroscopic and microscopic characteristics of recycled carbon fiber (rCF) during CFRP pyrolysis by innovatively combining typical experiments with machine learning. We first comprehensively studied the effects of treatment time and temperature on the mechanical properties, graphitization degree, lattice parameters, and surface O content of rCF following pyrolysis and oxidation. The surface resin residue was found to largely affect the degradation of the mechanical properties of the rCF, whereas oxidation treatment effectively removes this residue and is the critical recycling condition that determines its mechanical properties. In contrast, pyrolysis affected graphitization in a more-pronounced manner. More importantly, a random forest machine-learning model (RF model) that optimizes using a particle swarm algorithm was developed based on 336 data points and used to determine the mechanical properties and microstructural parameters of rCF when treated under various pyrolysis and oxidation conditions. The constructed model was effectively used to forecast the recovery conditions for various rCF target requirements, with the predictions for different recycling conditions found to be in good agreement with the experimental data.

Impact localization of CFRP laminate based on FBG sensing and GWO-BP neural network

Carbon fiber reinforced plastic (CFRP) structures are susceptible to external energy impact during service, resulting in invisible damage. Therefore, accurate localization of these impact sites is crucial for the maintenance and safety of CFRP structures. In this paper, the strain data of the impact response of the CFRP laminate is obtained by using the fiber grating (FBG) sensor, and the data are mapped to the impact location by BP neural network. However, the traditional BP neural network is susceptible to local minima and slow convergence speed. Therefore, Grey Wolf optimization algorithm (GWO) is introduced in this study to optimize the BP neural network, which improves the convergence speed and positioning accuracy of the model. The experimental results show that the GWO-BP neural network model has excellent performance in impact positioning, with the maximum positioning error of 17.01 mm and the average positioning error of 13.05 mm. Compared with other machine learning methods, it has higher accuracy and efficiency.

The Effect of Modifiers on the Strength and Impact Toughness of Carbon Fiber Reinforced Plastics

This study utilized epoxy resin, three types of fabric (carbon fiber, glass fiber, and Kevlar), and two plasticizers – tricresyl phosphate (TCP) and oleic acid (OA) – to enhance the impact toughness of carbon fiber-reinforced plastics (CFRP). The polymer matrix used in the experiments was a hot-cured epoxy compound "Etal Inject-T" consisting of two components: A – epoxy resin and B – hardener, in a mass ratio of 100:49.9. For the fabrication of CFRP plates, both manual and vacuum molding techniques were employed. Combined reinforcement of carbon fiber was achieved using one of two types of fabrics: Ortex 360 glass fiber or Kevlar. Accordingly, two compositions were prepared for the experiments: carbon fiber/glass fiber and carbon fiber/Kevlar. Layer stacking in each composition was performed at ratios of 10:10 and 14:6, consisting of 20 layers in total. The greatest strengthening effect for CFRP in the case of carbon fiber/glass fiber was observed with a layer ratio of 14:6 and matrix modification using 10% TCP plasticizer. The strength of the CFRP increased from 425 to 451 MPa, and the impact toughness (α) improved from 192 to 280 kJ/m². A key feature of this technology is the achievement of high-performance dual-purpose CFRP. This enables the reduction of CFRP structures in aerospace applications by 3 to 5 times, while simultaneously enhancing resistance to impact loads.

Assessment of occupational exposure to micro/nano particles generated from carbon fiber-reinforced plastic processing.

Carbon fiber-reinforced plastics (CFRP) are leading functional materials with superior strength and low mass density compared to metal. Our previous factory site analyses found that CFRP processing generates fibrous debris and fine micro/nano-sized particles of various shapes. The present interventional study was conducted at a factory located in Japan and evaluated debris consisting of various-sized particles generated during the industrial processing of CFRP, such as cutting, grinding, and turning of CFRP pipes, using real-time particle monitoring devices of the following: PM4 Digital Dust Monitor (DDM), handled Optical Particle Counter (OPC), Condensation Particle Counter (CPC), and Scanning Mobility Particle Sizer (SMPS). In addition, personal exposure of workers was evaluated using a novel wearable PM2.5-compatible device (P-sensor). First, we confirmed the presence of micro/nano particles in the dust generated during industrial processing of CFRP. Finer CFRP-generated particles were detected by the nanoparticle-compatible devices; CPC and SMPS, but not by OPC or DDM. The dynamic detection pattern of the P-sensor resembled that recorded by the nanoparticle-compatible devices. The novel wearable P-sensor can be used to measure finer particles generated by CFRP processing in occupational settings. Second, the exposure assessment was conducted twice and the levels of the micro/nano particles in the second survey were significantly (less than half) lower than that in the first survey. By avoiding immediate power-off of the exhaust system after operations, the scattering of particles was effectively reduced. Our results indicate that effective use of local exhaust ventilation system improves the workplace environment for particle exposure.

Bonding strength of short carbon fiber reinforced PA6 composites ultrasonic welding joints

AbstractShort carbon fiber composites have high specific modulus, high specific strength and excellent formability, which are suitable for short‐term and environmentally friendly ultrasonic welding. In this study, ultrasonic welding experiments were conducted on short carbon fiber reinforced PA6 composites using a servo‐driven non‐metallic ultrasonic welder. The weld specimens were analyzed for spot weld morphologies, interfacial bonding strength, failure modes and compressive shear fracture to establish the correlation between weld energy and weld quality. The results indicate that when the welding energy is 450 J, the short carbon fibers are interlaced at the interface, which has a pinning effect, thereby enhancing the joint strength reaching 2990 N, and the welding efficiency reaches 46%. Besides, the compressive shear section is divided into regions. The proportion of heat and area in different compressive shear regions are quantitatively analyzed, and the calculation model of joint strength is proposed. Meanwhile, the correlation between welding energy and its correlation, as well as three different shear failure modes, is obtained. This work is helpful for the optimization of carbon fiber reinforced thermoplastic (CFRTP) ultrasonic welding process parameters and the evaluation of joint strength.Highlights A special fixture for ultrasonic welding was designed to optimize the welding energy process of ultrasonic welding CFRTP, and the properties of ultrasonic welded joints were analyzed. When the welding energy is 450 J, a high shear strength welded joint of 2990 N was successfully prepared. The shear fracture of the welded joint was successfully obtained by designing a special clamp for compression shearing. The welding area was qualitatively divided according to the temperature measurement results of the interface center. In addition, the heat and area ratio of different zones in the compression‐shearing area under different welding energy were quantitatively analyzed, and the calculation model of joint strength was proposed. At the same time, the welding energy and its correlation, as well as three different shear failure modes were obtained. The short fiber distribution characterization and pores effect on the joint's strength were analyzed. The appearance of porosity defects has a significant weakening effect on the welded joint.

Dual-Modal Fusion PRI-SWT Model for Eddy Current Detection of Cracks, Delamination, and Impact Damage in Carbon Fiber-Reinforced Plastic Materials

Carbon fiber-reinforced plastic (CFRP) composites are prone to damage during both manufacturing and operational phases, making the classification and identification of defects critical for maintaining structural integrity. This paper presents a novel dual-modal feature classification approach for the eddy current detection of CFRP defects, utilizing a Parallel Real–Imaginary/Swin Transformer (PRI-SWT) model. Built using the Transformer architecture, the PRI-SWT model effectively integrates the real and imaginary components of sinusoidal voltage signals, demonstrating a significant performance improvement over traditional classification methods such as Support Vector Machine (SVM) and Vision Transformer (ViT). The proposed model achieved a classification accuracy exceeding 95%, highlighting its superior capability in terms of addressing the complexities of defect detection. Furthermore, the influence of key factors—including the real–imaginary fusion layer, the number of layers, the window shift size, and the model’s scale—on the classification performance of the PRI-SWT model was systematically evaluated.

Optimized design of energy absorption of aluminum foam filled CFRP thin-walled square tube based on agent model

PurposeAluminum foam-filled thin-walled unit structures have received much attention for their excellent energy absorption properties. To improve the energy absorption effect of car energy absorption box under axial compression, this paper optimizes the fiber lay-up sequence, fiber angle and aluminum foam density of aluminum foam filled carbon fiber reinforced plastic (CFRP) thin-walled square tubes.Design/methodology/approachDesign of sample points required to construct the proxy model using design of experiments (DOE) method, and the data sample points of different models are obtained through Abaqus simulation and test. A double high-precision proxy model with the maximum specific energy absorption (SEA) and the minimum initial peak crash force (PCF) as the evaluation index is constructed based on the response surface function method. The NSGA-II multi-objective genetic algorithm was used to optimize the design parameters and obtain the optimal solution for the Pareto front, and the results were verified by using the multi-objective optimization toolbox in design-expert.FindingsThe results show that the optimal solution to the multi-objective optimization problem with the inclusion of the lay-up sequence is ρ = 0.5g/cm3 for a fiber lay-up angle varying in the range ±15–90° and an aluminum foam density varying in the range 0.2g/cm3-0.5g/cm3, with a lay-up method of [±87°/±16°/±15°/±89°]. The two optimization methods correspond to SEA and PCF errors of 2.109% and 4.1828%, respectively. The optimized SEA value is 18.2 J/g and the PCF value is 18,230 N. The optimized design reduces the peak impact force and increases the specific energy absorption, which improves the energy absorption effect of thin-walled energy-absorbing boxes for automobiles.Originality/valueIn this paper, the impact resistance of CFRP thin-walled square tubes filled with aluminum foam is optimized. Based on numerical simulations and experiments to obtain the sample point data for constructing the dual-agent model, we investigate the effect of filling with different densities of aluminum foam under the simultaneous change of fiber lay-up angle and order on its mechanical properties in this process, and carry out the multi-objective optimization design with NSGA-II algorithm.

Exceptional Damping of CFRPs: Unveiling the Impact of Carbon Fiber Surface Treatments

A significant body of research has concentrated on augmenting the interfacial slippage between carbon fiber (CF) fabrics and resin to improve the damping properties of CF reinforced plastics (CFRPs). However, a critical but commonly overlooked issue lies in the fact that a higher number of mutual slippage interfaces may lead to potential debonding at the interface. This, in turn, not only influences mechanical properties but also escalates the risk of structural damage. In this study, three distinct modification methods are proposed to alter the surface characteristics of CFs, achieving simultaneous enhancement of the damping and mechanical properties of CFRPs. Specifically, the polymerization of dopamine on the CF surface results in CFRPs with a loss factor of 0.227 at 1 Hz, which is 180% higher than the 0.081 of referenced CFRP. Additionally, using this modification method, the tensile modulus and strength of the CFRP are increased by about 11.3% and 6.7%, respectively. More importantly, the subtle relationships within the CFRPs interface are explored through the different surface modification methods. The interactions between surface wettability, specific surface area, roughness, and interfacial chemical bonding on the mechanical and damping properties are redefined and elucidated. This study demonstrates that surface modification can simultaneously enhance both the damping and mechanical properties of CFRPs, and a satisfactory balance of multiple enhancement effects can be achieved by adjusting the surface modification factors.

High-throughput in-situ mechanical evaluation and parameter optimization for 3D printing of continuous carbon fiber composites

The mechanical properties of carbon fiber reinforced thermoplastic (CFRTP) molded parts produced by thermal fusion lamination 3D printing vary with printing conditions. This study assesses the influence of the 3D printing parameters on the mechanical properties of resulting CFRTP products through parameter evaluation testing. An in-situ three-point bending test mechanism was developed to enhance the efficiency of these tests, allowing the same 3D printer to handle all processes from printing multiple CFRTP specimens simultaneously to conducting a bending test, reducing manual handling time to about one minute. Using this modified 3D printer, 700 specimens with varying printing conditions were produced, and their flexural strength was measured semi-automatically. Results revealed that the flexural strength of the 3D-printed CFRTP object varied with nozzle temperature, printing pitch, and stacking pitch, but not with printing speed. Machine learning was then employed to predict the maximum flexural strength and determine optimal printing parameters using the collected data as training data.

Evaluating the moisture absorption percentage in the adhesive layers of carbon fiber reinforced plastic joints via electromagnetic induction testing and diffusion analysis

In this study, we propose a method that combines moisture absorption analysis and electromagnetic induction testing (EIT) to detect weak bonds formed in the adhesive layers of carbon fiber reinforced plastic (CFRP) joints due to moisture absorption. Although EIT is effective for assessing CFRP joint adhesive moisture absorption, early detection of weak bonds is challenging. Our method addresses this by combining calculations of moisture absorption percentage via the diffusion equation and EIT-based measurements. First, we conducted diffusion analysis using the finite difference method (FDM). The moisture absorption percentage calculated via the FDM was validated by comparing it with the results of the moisture absorption tests of the CFRP adhesive joints, and good agreement was observed. Second, EIT was applied to the CFRP adhesive joints, and the experimental results indicated that the moisture absorption percentage of the CFRP joint adhesive layer can be qualitatively evaluated using EIT. Finally, the moisture absorption of the CFRP joint adhesive layer was quantitatively evaluated by combining the results of EIT on the CFRP joints with those of the numerical analysis. Therefore, the proposed method enables the previously difficult quantitative evaluation of the initial moisture absorption percentage of CFRP joint adhesive layers and is effective for the early detection of weak bonds created by moisture absorption in CFRP joints.

Seismic performance enhancement of underground structure influenced by CFRP wrapping schemes

The seismic failure mechanism of underground structures showing structural collapse is attributed to the deformation incompatibility between the central columns and the sidewalls, especially the insufficient deformation capacity of the central columns. Therefore, the approaches to improve the seismic performance of underground structures mainly aim at avoiding the damage of central columns. This paper presents a comparative study on the seismic performance of underground structures with the central columns retrofitted with different numbers of Carbon Fiber Reinforced Plastics (CFRP) layers. Seismic capacity of the CFRP retrofitting reinforced concrete (RC) columns was explored in detail through experimental and numerical approaches. Then numerical models were built and verified to simulate the behaviors of the CFRP retrofitting RC columns, and the seismic performance of the CFRP retrofitting underground structures was numerically simulated. Based on the numerical results, the damage of the CFRP retrofitting underground structures was calculated, and damage classification illustrated that the seismic performance of underground structures was enhanced remarkably. Finally, parametric studies were conducted to discuss about how the number of CFRP layers and the earthquake intensity affect the earthquake-induced damage of underground structures. Conclusions from this study could be referenced for seismic retrofitting of existing underground structures.

Detection of matrix cracks in composite laminates using embedded fiber-optic distributed strain sensing

We used an optical frequency-domain reflectometry fiber Bragg grating (FBG) distributed strain measurement system to detect matrix cracks in carbon fiber-reinforced plastics. Load/unload tests were performed on cross-ply laminates under ambient-, high-, and low-temperature conditions using embedded 100mm gauge FBG sensors. We measured the strain distributed along the gauge and examined the crack initiation using optical microscopy and soft X-ray inspection. A peak appeared in the distributed strain corresponding to the occurrence of cracks. The strain distribution during crack initiation was calculated in each temperature range via finite element analysis and compared with the measurement results. We determined that the number and location of cracks can be determined by extracting the peaks from the distributed strain. However, the peaks in the distributed strain do not correspond to the occurrence of cracks that are close to each other, warranting a measurement method with a higher strain resolution.

On the Influence of Welding Parameters and Their Interdependence During Robotic Continuous Ultrasonic Welding of Carbon Fibre Reinforced Thermoplastics.

Ultrasonic welding of fibre-reinforced thermoplastics is a joining technology with high potential for short welding times and low energy consumption. While the majority of the current studies on continuous ultrasonic welding have so far focused on woven reinforcements, unidirectional materials are preferred for highly loaded aerospace components due to their better mechanical performance. Therefore, this paper investigates the influence and interdependence of the welding speed, amplitude, and energy director thickness on the weld quality of adherends made of unidirectional composites. The quality of the welded joints is assessed by a single-lap shear strength and fracture surface analysis complemented by the microscopic analysis of cross-sections and comparison to a co-consolidated reference. The results showed that the welding process is highly affected by changing welding speeds for a given amplitude. Furthermore, while lower amplitudes lead to significant scatter in the welding quality, higher amplitudes result in increased heating rates and a fully molten energy director even for high welding speeds. Nevertheless, insufficient consolidation at high welding speeds results in porosity in the weld line. Finally, it was observed that thicker, and therefore more compliant, energy directors lead to more uniform melting of the energy director and less deviation in the weld quality for a wider range of welding speeds.

Experiments for the Assessment of the Probabilistic Multistage Algorithm for Damage Detection in Flat and Curved CFRP Panels

The Probabilistic Multistage (PM) algorithm was developed by authors for identifying and localizing damages in isotropic plates. More specifically, PM algorithm consists of a fully automated probabilistic damage imaging methodology based on ultrasonic guided wave propagation and a 5-sensor array working in a pitch-catch approach. The algorithm processes the gathered data in three steps to identify key features associated with damage. The aim of this work is to assess the effectiveness of the PM algorithm on more complex structures. Specifically, the study investigates three cases of study, made of Carbon Fiber Reinforced Plastic (CFRP) composite, characterized by different geometries and layups. The algorithm is initially tested on panels with artificial damage in the form of a Teflon disk located in a specific location between the middle laminae of the panels, which is used to replicate the effect of delamination. In order to expand the experimental dataset without incurring additional costs or waste, new damage conditions are simulated by adding masses on the upper surface of the panels. Each plate is investigated considering three different damage sizes and 16 different damage locations. The proposed algorithm successfully detects damages both within and outside the sensor network. The PM algorithm produces a clear damage positioning map and a positioning (probabilistic field) range for the identified damage. This information can be used to assist operators in conducting inspections more efficiently by focusing on the highlighted areas, which may potentially lead to reduced maintenance and repair expenses.

Dynamic compressive impact behavior of CFRP composite under high strain rate loading

Carbon Fiber Reinforced Plastics (CFRP) composite exhibits significant sensitivity to strain rate. In this work, authors investigated the CFRP composite’s dynamic compressive impact behavior under a high strain rate. The vacuum bagging technique was used to fabricate the composite specimens to reduce the presence of voids. Compressive mechanical behavior was examined for the CFRP composite for strain rate ranging from 1384 to 1953 s−1 using a Split Hopkinson Pressure Bar (SHPB) test setup. The experiment aims to investigate the effect of strain rate on stresses, strains, failure strengths, fracture energies, and failure of quasi-isotropic CFRP composites. Furthermore, the specimens’ strength initially increases and achieves its maximum strength, followed by a decrease in strength value. Moreover, an optical microscopic examination reveals that damage in composite laminate grows as the strain rate increases.

Classification, Localization and Quantization of Eddy Current Detection Defects in CFRP Based on EDC-YOLO.

The accurate detection and quantification of defects is vital for the effectiveness of the eddy current nondestructive testing (ECNDT) of carbon fiber-reinforced plastic (CFRP) materials. This study investigates the identification and measurement of three common CFRP defects-cracks, delamination, and low-velocity impact damage-by employing the You Only Look Once (YOLO) model and an improved Eddy Current YOLO (EDC-YOLO) model. YOLO's limitations in detecting multi-scale features are addressed through the integration of Transformer-based self-attention mechanisms and deformable convolutional sub-modules, with additional global feature extraction via CBAM. By leveraging the Wise-IoU loss function, the model performance is further enhanced, leading to a 4.4% increase in the mAP50 for defect detection. EDC-YOLO proves to be effective for defect identification and quantification in industrial inspections, providing detailed insights, such as the correlation between the impact damage size and energy levels.

Strength characteristics analysis and unified model establishment of bonded, riveted, and adhesive-rivet hybrid CFRP joints

Carbon fiber-reinforced plastic (CFRP) bonded, riveted, and adhesive-rivet hybrid joints are among the weak parts of a structure. To study their strength characteristics and strength characterization methods, the key parameters of the CFRP constitutive model were obtained by constitutive tests, based on which finite element (FE) models of the three types of CFRP joints were established. The failure modes and F–D (load-displacement) curves of the CFRP joints were obtained through quasi-static tensile tests, and the accuracy of the FE models was verified. Based on the test results, the test strength models of the three types of CFRP joints were established, along with a unified model to characterize the joint strength; however, the strength models were affected by the joint parameters. Therefore, the influence of nine parameters on the joint strength were discussed, and the unified strength models of the three types of CFRP joints under each influencing parameter were established based on the initial strength model. The results showed that the unified strength models were highly correlated with R2 > 0.92. These results provide useful references for the strength check and parameter optimization of CFRP joints.

Smart carbon fiber reinforced plastic tubes with electromechanical structural health self-monitoring system

Carbon fiber-reinforced plastics (CFRPs), are utilized in the various mobilities owing to their superior specific structural rigidity. However, the studies for the structural health monitoring (SHM) of a tubular CFRP are relatively limited compared to its market share although it is directly related to the structural duties. Thus, this study investigated structural health self-monitoring (SHSm) of tubular CFRPs by monitoring their electrical resistances simultaneously. Multiple electrodes monitored electrical resistances of the CFRP tubes in terms of their mechanical deformation realizing condition-based SHM without any sensors. The proposed self-monitoring technology enabled not only local but also global deformation even after the mechanical fractures. Furthermore, the proposed self-monitoring method was comparatively analyzed and verified with corresponding mechanics and finite element analysis. Therefore, real-time SHSm technology for tubular CFRPs with various fiber stacking configurations was realised in this study covering all the deformation types over the whole structures.

Delamination and thrust force mitigation during drilling of unidirectional carbon fibre-reinforced plastic laminate using magnetorheological elastomer

Abstract In new improvements to the aviation industry, carbon fibre-reinforced plastic (CFRP) is a buoyant material due to its noteworthy and application-friendly properties. The behaviour of transversely isotropic CFRP, which prompts drilling-induced delamination, causes critical damage that leads to the rejection of the final product. The cause of the delamination damage is the thrust force generated by the drilling tool during the machining operation. The present work proposes an indigenous approach to suppress delamination significantly using magnetorheological elastomer (MRE). The thrust force generated by the drilling tool is recorded for varying magnetic field strengths. Delamination damage was computed using the MATLAB script. Meanwhile, specific focus was given to studying the interlaminar mechanics of a drilled hole through scanning electron microscopy. The results show that nearly 45% of the thrust force is reduced using this MRE at a maximum field strength of 0.4T compared to a conventional one. The results are further supported by a 22% and 30% smoothening of the delamination at the hole’s entry and exit, respectively. Thus, this approach helps to reduce delamination during drilling.