Carbon Fiber Reinforced Plastics Samples Research Articles

Multiscale modeling for viscoelasticity of woven CFRP considering preforming and curing effects via finite element and long-short term memory analysis

The constitutional model is crucial to model the complex response of woven carbon fiber reinforced plastics (CFRPs) during curing. This study developed a multiscale modeling method to describe the viscoelastic behavior of preformed woven CFRP parts during curing. The essence of the viscoelastic modeling lies in the stiffness matrix, which was predicted via the long-short term memory (LSTM) model. A library of viscoelastic stiffness components for the LSTM model was created through finite element method (FEM) on mesoscopic representation volume elements (RVEs). Specifically, FEM models for RVEs with different configurations were established first, and then utilized for virtual relaxation tests to build the stiffness component library for the woven CFRP. Afterwards, the LSTM model was embedded into the material model for FEM at the part-level. Finally, the prediction accuracy of the multiscale modeling method for viscoelasticity of the woven CFRP samples was validated by experiments with the average error of 6.62%.

EHSGNet: A novel edge and high-level semantic guided network for CFRP subsurface defects detection

Infrared thermography, due to its fast detection speed, low cost, and intuitive results, is widely used in carbon fiber reinforced plastics (CFRP) materials detection. However, CFRP infrared image noise and lower resolution lead to intrinsic similarly and edge disruption, making it impossible to obtain satisfactory results. Additionally, the current methods require a lot of preprocessing and post-processing operations. Since camouflaged object detection has achieved good results in solving intrinsic similarly and edge disruption. Therefore, this paper proposes an edge and high-level semantic guided network (EHSGNet) based on camouflaged object detection for infrared thermography CFRP subsurface defect detection, achieving end-to-end detection of CFRP. This paper also constructed a dataset containing CFRP samples with both natural defects and artificially simulated internal defects to validate our method. Since the temperature change is achieved in the temperature-controlled chamber by means of hot air heating, it can heat the sample uniformly and improve the quality of CFRP infrared images, so our experiments will be carried out in the temperature-controlled chamber. The experiments were conducted on CFRP specimens with different sizes, shapes and depths of artificial defects. The experimental results demonstrate that the proposed EHSGNet outperforms current approaches significantly on our dataset, with precision (PRC) and recall (RCL) reaching 94.3% and 93.1%, respectively.

Optimizing the manufacturing technology of high-strength fiber reinforced composites based on aluminophosphates

Phosphate copolymers show promise for composite materials, but using polymer binders for structural composites is limited by underdeveloped manufacturing processes, resulting in low strength. This work optimized the technological process for obtaining carbon fiber reinforced plastics (CFRPs) based on aluminophosphate (AP), aluminoborophosphate (ABP), and aluminochromophosphate (ACP) binders. Carbon fiber impregnation regimes were optimized considering inorganic binders’ rheological properties, density, surface tension, and wettability. A rotary rheometer evaluated phosphate binder tack to optimize molding modes. The compaction dependency of woven phosphate binder prepregs on pressure was evaluated, selecting optimal CFRP molding modes. TGA-DSC analysis optimized phosphate binder drying and curing processes. CFRP samples with 55–58 % volume AP, ACP, and ABP binders were obtained by vacuum molding, exhibiting high tensile, flexural strength and modulus. High heat resistance was shown by dynamic mechanical analysis, and thermal shock resistance evaluated by residual flexural strength change.

A pre-screening of the solvolysis recycling process for CFRPs based on the mechanical properties of the recovered fibers.

Over the past few decades, composite materials, and specifically carbon fiber reinforced plastics (CFRPs), are finding increasing use in the automotive, aerospace, and aeronautics industries. As a result, the production of CFRPs has been significantly increased, thus leading to a corresponding increase in waste production. In the near future, landfill and incineration disposal of waste will likely be prevented due to legislation, thereby bringing forward the need to develop efficient recycling processes for CFRPs. However, recycling of CFRPs is very challenging, mainly due to the difficulty in removing the thermosetting matrix. This paper reports a pre-screening of the solvolysis recycling process for CFRPs on the basis of the mechanical properties of the recovered fibers. To this end, solvolysis tests were conducted on unidirectional CFRP samples under supercritical and subcritical conditions using acetone and water. The solvolysis tests were conducted for various conditions of temperature, pressure, and reaction time. The efficiency of the recycling processes has been evaluated by means of single-fiber tension tests on the recovered fibers, which were conducted according to the ASTM C 1557-14 standard. In most cases, the decomposition efficiency of the epoxy resin in the CFRP, measured in terms of mass, ranged between 90 and 100%. Moreover, the mechanical tests showed that the recovered fibers retained more than 58% of their initial Young’s modulus and tensile strength.

A Preliminary Investigation on a Water- and Acetone-Based Solvolysis Recycling Process for CFRPs.

Composites, and especially carbon-fiber-reinforced plastics (CFRPs), are increasingly used in the automotive, aerospace, and aviation industries, and as a result, CFRP production has increased dramatically, leading to a corresponding increase in waste. Landfills and the incineration of waste are likely to be restricted as a result of legislation, thus highlighting the need for efficient recycling methods for CFRPs. However, the recycling of CFRPs is very challenging, mainly due to the difficulty of removing their thermosetting matrix. This study reports a pre-screening of the solvolysis recycling process for CFRPs based on the mechanical properties of the recovered fibers. To this end, solvolysis tests were conducted on unidirectional CFRP samples under supercritical and subcritical conditions using acetone and water. The solvolysis tests were conducted for various conditions of temperature, pressure, and reaction time, without the use of any catalyst. Also, the loading rate (volume of solvent/volume of reactor) was constant. The efficiency of the recycling processes has been evaluated through a morphological and a mechanical characterization of the recovered fibers. In most cases, the decomposition efficiency of the epoxy resin, measured in terms of mass, ranged between 90 and 100%. Moreover, the scanning electron microscopy images of the recovered fibers showed negligible traces of resin residues and no detectable signs of physical damage or any changes in morphology with regard to diameter. Finally, the single-fiber tension tests revealed that that the recovered fibers retained more than 61% of their initial Young's modulus and 70% of their tensile strength.

Few-shot meta transfer learning-based damage detection of composite structures

Damage detection and localization using data-driven approaches in carbon fiber reinforced plastics (CFRP) composite structures is becoming increasingly important. However, the performance of conventional data-driven methods degrades greatly under little amount of data. In addition, the scarcity of data corresponding to defect/damage conditions of CFRP structures lead to extreme data imbalance, which make this problem even more challenging. To address these challenges of few training data and the scarcity of damage samples, this paper proposes a few-shot meta transfer learning (FMTL)-based approach for damage detection in CFRP composite structures. This method leverages knowledge learnt from an unbalanced data domain generated from a single CFRP composite sample and adapts the knowledge to be applied for other data domains generated by CFRP samples with different structural properties. The contributions of this research include demonstrating the feasibility of harnessing knowledge from notably limited experiment data, designing an algorithm for configuring hyperparameters based on a specific FMTL task, and identifying the impacts of hyperparameters on learning performances. Results show that FMTL can improve the recall rate by at least 15% while preserving the ability to identify health conditions. This method can be extremely useful when we need to monitor health condition of critical CFRP structures, like airplanes, because they can rarely generate data under damage conditions for model training. FMTL enables us to build new models based on unbalanced source domain data with the cost of a minimal set of samples from the target domain.

Detection of flat-bottom holes in composite materials using multi-dimensional complementary ensemble empirical mode decomposition algorithm

Due to high-temperature resistance, high strength, and excellent fatigue resistance, composite materials are widely used in automotive manufacturing, aerospace, infrastructure and other fields. Consequently, the demand for defect detection of composite materials is also increasing. As a non-destructive testing technique, the active infrared thermography, which can achieve full-field defect detection, is suitable for defect detection of composite materials. However, this method is susceptible to noises caused by the environment and heating sources. In order to solve the problem of the defect signal being submerged by these noises, a multi-dimensional complementary ensemble empirical mode decomposition (MCEEMD) algorithm is introduced in this paper. This method can decompose the signal into the low-frequency background noise, the high-frequency heating noise, and useful defect signals, and these noises can be easily removed to improve the contrast to noise ratio (CNR) of defect images. Based on this proposed method, a defect detection experiment on the carbon fiber reinforced plastic (CFRP) is performed in this paper, and experimental results show that the method can effectively remove environmental noise and heating noise, and it can detect 11 out of 12 defects on the CFRP sample with an average CNR of 9.107. Compared with the traditional differential absolute contrast method, this method can detect one additional small defect with the aspect ratio of 1.67 and one deep defect with a depth of 2 mm.

Nondestructive evaluation of cork phenolic-based aerospace structure using Terahertz time domain spectroscopy and imaging

ABSTRACT Cork phenolic sheets with thermal protection paints are used as thermal protection system in launch vehicles. Detecting debonds and inclusions at the interface of cork phenolic-composite structure and thickness measurement of multi-layer TPS is highly challenging with single-side access. In this study, Terahertz imaging in reflection mode is used to detect the defects. The sample is raster scanned in X-Y directions, and the reflected signal is measured at each pixel and consolidated to obtain a Terahertz C-scan image of the entire area. The image obtained using Terahertz time-domain spectroscopy imaging system can distinguish between the healthy and debond/inclusion areas. Terahertz time-domain spectroscopy in transmission mode is carried out to obtain the refractive index and absorption coefficient of individual layers, which is a prerequisite for thickness measurement. The time-of-flight principle is used for thickness estimation of multi-layer thermal protection systems over carbon fibre reinforced plastic samples. The Sparse deconvolution technique is used to resolve the overlapping echoes to obtain the time delay for each layer. The thickness values obtained for cork phenolic sheet and red and white thermal protection paints over carbon fibre-reinforced plastic samples using Terahertz-time domain spectroscopy are in close agreement with the actual values.

Carbon Fiber Reinforced Plastics Based on an Epoxy Binder with the Effect of Thermally Induced Self-Repair

The authors have proposed the novel approach for evaluation of the self-healing effect in carbon fiber reinforced plastics (CFRP) on micro- and macro samples, using the dynamic mechanical analysis (DMA) and the double-cantilever beam delamination methods, respectively. A modified epoxy resin with a self-healing effect was used as the matrix for carbon plastics. The flexural modulus E’ of microsamples with delamination and the specific delamination energy (crack resistance) GIR of macrosamples with a given initial crack were chosen as criteria for evaluating the self-healing of carbon plastics. The sensitivity of the E’ and GIR parameters to the applied initial crack is shown. The value of the elastic modulus E’ with the initial crack can be reduced up to two times compared to the E’ values for the control materials, depending on the length of the initial crack. The degree of recovery of E’ for CFRP with a microcrack varies from 91 to 118%. A high degree of healing could be achieved in 48 h. The GIR value of CFRP samples with a given macroseparation after heat treatment is 7% of the initial GIR value (0.7 kJ/m2). Recovery of delaminations for microsamples is more efficient than for macrosamples. The study of CFRP cracks by X-ray tomography before and after self-healing showed that the crack “overgrows” during the heat treatment cycle, and the defects (pores) formed during the manufacture of the sample decrease in size.

Defect Detection and Depth Estimation in Composite Materials for Pulsed Thermography Images by Nonuniform Heating Correction and Oriented Gradient Information.

Pulsed thermography is a nondestructive method commonly used to explore anomalies in composite materials. This paper presents a procedure for the automated detection of defects in thermal images of composite materials obtained with pulsed thermography experiments. The proposed methodology is simple and novel as it is reliable in low-contrast and nonuniform heating conditions and does not require data preprocessing. Nonuniform heating correction and the gradient direction information combined with a local and global segmentation phase are used to analyze carbon fiber-reinforced plastic (CFRP) thermal images with Teflon inserts with different length/depth ratios. Additionally, a comparison between the actual depths and estimated depths of detected defects is performed. The performance of the nonuniform heating correction proposed method is superior to that obtained on the same CFRP sample analyzed with a deep learning algorithm and the background thermal compensation by filtering strategy.

High-efficiency ultrasound generation excited by an annular distributed Ho:YAG laser with 2.09 µm in carbon fiber reinforced plastics

In this paper, we demonstrate a high-efficiency laser ultrasound in carbon-fiber-reinforced plastics (CFRP) by means of excitation laser spatial distribution modulation. The maximum ultrasound signal amplitude of about 286 mV was achieved by directing an annular laser with wavelength of 2090 nm, energy of 2.5 mJ and pulse width of 16 ns onto CFRP samples. The ultrasonic signal amplitude excited by annular laser is more than one times higher than that by typical Gaussian laser under the same conditions. Also, for CFRP laser ultrasound excited by annular laser, the intensity of ultrasound increased in different frequency components as a whole with increasement of laser energy density. The results indicated that it is an effective technical way to improve photo-acoustic efficiency and amplitude of the ultrasonic signal by shaping 2 μm region excited laser spatial distribution into annular type in the CFRP laser ultrasound.

Determination of Local Electrical Properties Using a Potential Field Measurement for Electrically Conductive Carbon Fiber Reinforced Plastics with Metal Contact Pins Joined via Injection Molding.

Carbon fiber reinforced plastics (CFRP) bear a high potential in terms of electrical conductivity and its potential applications. A locally resolved electrical measurement method for these anisotropic materials is a key prerequisite for understanding the structural and manufacturing process-related interrelationships. The aim of this paper is to develop a measurement method that allows this to be achieved and also to investigate areas of overmolded metal contact pins in detail. CFRP samples with polyamide 6 and polycarbonate matrices were used, which were produced by using a custom-designed injection mold. In order to evaluate the measurement results and to correlate them to process related structural properties, reflected light microscopy and X-ray microtomography were used. Typical areas with significant fiber structures of assembly injection molded samples were electrically and structurally characterized to identify correlations. Among further results, the correlation of equipotential lines, acquired from the electrical analysis, with specific fiber orientations within the injection molded samples was demonstrated, fiber-poor areas were identified, and a beneficial influence of weld lines on contact resistances was determined.

Three-axis displacement sensor-integrated spindle system for carbon fiber-reinforced plastic (CFRP) machining process monitoring

We present a carbon fiber-reinforced plastic (CFRP) milling process monitoring method based on the multi-axis displacement sensor integrated with the precision spindle system. Unlike conventional machining process monitoring methods by using accelerometers or dynamometers, in this study, 2-axis curved-edge sensor (CES) and single-axis capacitive sensor (CS) were integrated with the spindle and measured 3-axis spindle shaft motions along the radial and axial directions, respectively, while machining. Also, the CES showed a status of the spindle balancing while correcting the unbalanced mass on the spindle. The 3 × 3 stiffness matrix of the spindle shaft was multiplied by the 3-axis spindle shaft motions, and simultaneously estimates the cutting forces while cutting the unidirectional CFRP samples with 0o, 45o, and 90o fiber orientation. The conventional 3-axis force sensor installed under the workpiece was used for a baseline comparison with the cutting force results measured by the proposed method. To monitor and characterize the defects of CFRP, the time-frequency domain spectrogram images analyzed from the cutting force data were used. Unique features were extracted according to the fact of existence and nonexistence of the defects.

Estimation of fracture mode of CFRP by machine learning of AE features in frequency domain

The aim of this study is to classify the damages in a Carbon Fiber Reinforced Plastics (CFRP) plate under tensile loading with a machine learning technique for acoustic emission (AE) waveforms. AE waveforms originate from micro cracks inside a material and their features are characterized by fracture mode. In this study, waveform energy in each frequency band obtained with the wavelet transform were used as features for classifying AE waveform using self-organization map(SOM) and k-means. Furthermore, the validity of the classification was evaluated by cross-sectional observation and by AE in unidirectional CFRP samples.

Flexible ECT probe with front-end differential setting for inspection of curved CFRP structures

Carbon-fiber-reinforced plastic (CFRP) structures with curved surfaces are widely used. However, an efficient tool for curved CFRP inspection is still lacking. This paper proposes a flexible eddy current testing (ECT) probe with front-end differential setting designed specifically for curved CFRP structures inspection. The probe contains a pair of coils that are fabricated on a flexible printed circuit board . It can be bent to fit a curved surface without the need to redesign the probe. The two coils of the probe work simultaneously and highlight a defect's signal by the front-end differential configuration. It effectively suppresses common mode interference , which is generally evitable in the experiment of CFRP structures inspection using a high frequency ECT system. A finite element method (FEM) model is established to study the inspection principle in viewing of the anisotropic electrical conductivity of CFRP. A prototype probe is fabricated and tested, with which a flat and three curved CFRP samples with machined and natural impact cracks are imaged. A signal processing algorithm including detrending, gradient calculation and nonlinear transformation is developed to improve the probe's defect detectability . Experiment results show that a small defect located on the curved surface of a CFRP sample with dimensions: length 2 mm, width 1 mm and depth 0.4 mm can be detected by the probe. In addition, the effects of the operating frequency and orientation of the defect are discussed.

ИССЛЕДОВАНИЕ ВОЗМОЖНОСТИ ПОВЫШЕНИЯ СТОЙКОСТИ К УДАРУ ТОНКОЛИСТОВЫХ УГЛЕПЛАСТИКОВ ЗА СЧЕТ ПЛАКИРОВАНИЯ АРАМИДНЫМ ОРГАНОПЛАСТИКОМ

The article provides the analysis of the scientific and technical literature and the effectiveness of the use of organoplastics in aircraft structures as screens and coatings that provide protection of carbon parts from damage caused by shock and erosion. The paper examined the mechanical properties and the nature of impact fracture of carbon fiber-reinforced plastics samples without a cladding layer. It is shown that it is possible to increase the specific impact strength in bending by 22 % due to cladding of CFRP, and to reduce the area of damage by 30 % upon impact with a kinetic energy of 30 J.

Self-resonance eddy-current testing method in noncontact detection of carbon fiber reinforced plastics

Eddy-current testing (ECT) is relatively mature and has gradually become one of the main non-destructive testing technologies of carbon fiber reinforced plastics (CFRP). From the perspective of low conductivity of CFRP, high-frequency ECT technology is considered to be more suitable for such materials with less conductivity. However, high-frequency ECT method is more susceptible to various interference factors and has higher background noise, which will cause great inconvenience to practical applications. Concerning this issue, a self-resonance ECT method in noncontact detection of CFRP was proposed in this paper, and the corresponding ECT system that can detect the defects of CFRP at a low-frequency and keep a relatively high signal-to-noise ratio was developed. The system is mainly composed of a resonant eddy-current probe, a special excitation circuit, and an ECT signal pickup module. Three CFRP samples (orthogonal woven laminate, cross-ply laminate, four-directional angle-ply laminate) were scanned by using the self-resonance ETC system developed in this paper. The scanning results show that the detection effect of the self-resonant ECT system at 500 kHz is similar to that of the traditional ECT system at 10 MHz, and 500KHz is enough to provide good detection results for the above CFRP samples. Therefore, the proposed research is a valuable ECT technology, which has played a guiding role in the application of low-frequency ECT method in CFRP.

Experimental and analytical investigation of the drilling forces of the carbon fiber reinforced plastics including thermal effects

• We developed the numerical model of the CFRP drilling with the thermophysical properties. • Experimental validations of the drilling thrust force were performed. • The thrust force fluctuations during the CFRP drilling process were predicted. • The numerical model can capture the trend of the experiments with minimum error. We present the experimental and analytical investigation of the drilling forces for unidirectional carbon fiber reinforced plastics (CFRP) composites according to different machining parameters. In the analytical thrust force model, the chisel edge region is classified as an extrusion operation and the lip is considered as a orthogonal small-element cutting region. The chipping, pressing, and bouncing regions of the lip are incorporated into this thrust force model. In addition, the analytical model includes the thermophysical properties of CFRP by incorporating softening of the material due to drilling processes. Based on the developed model, the thrust force curve for all drilling stages is analyzed in time-domain considering both cutting conditions and material properties. Comparison between predictions and experiments was performed on three CFRP samples with different fiber volume fractions and sixteen cutting conditions. It was observed that the predicted forces can capture the trend of the experimental data with the relative small error. Time-domain analysis and fluctuation evaluation were also used to better understand the mechanism of thrust force during CFRP drilling.

Matrix Decomposition of Carbon-Fiber-Reinforced Plastics via the Activation of Semiconductors.

The present study proposed a novel process for the matrix decomposition of carbon-fiber-reinforced plastics (CFRPs). For this purpose, the influence of ultraviolet (UV) radiation paired with semiconductors on CFRP was analyzed. Then, suitable process parameters for superficial and in-depth matrix decomposition in CFRP were evaluated. The epoxy resin was decomposed most effectively without damaging the embedded carbon fiber by using a UV light-emitting diode (LED) spotlight (395 nm, Semray 4103 by Heraeus Noblelight) at a power level of 66% compared to the maximum power of the spotlight. Using a distance of 10 mm and a treatment duration of only 35–40 s achieved a depth of two layers with an area of 750 mm2, which is suitable for technological CFRP repair procedures. In addition to the characterization of the process, the treated CFRP samples were analyzed based on several analytical methods, namely, light microscopy (LM), scanning electron microscopy (SEM), and atomic force microscopy (AFM). Subsequently, the prepared carbon fibers (CFs) were tested using filament tensiometry, single filament tensile tests, and thermogravimetric measurements. All analyses showed the power level of 66% to be superior to the use of 96% power. The gentle (“fiber friendly”) matrix destruction reduced the damage to the surface of the fibers and maintained their properties, such as maximum elongation and maximum tensile strength, at the level of the reference materials.

Tensile and fatigue testing of impacted smart CFRP composites with embedded PZT transducers for nonlinear ultrasonic monitoring of damage evolution

Ultrasonic systems based on ‘smart’ composite structures with embedded sensor networks can reduce both inspection time and costs of aircraft components during maintenance or in-service. This paper assessed the tensile strength and fatigue endurance of carbon fibre reinforced plastic (CFRP) laminates with embedded piezoelectric (PZT) transducers, which were covered with glass fibre patches for electrical insulation. This sensor layout was proposed and tested by the authors in recent studies, proving its suitability for nonlinear ultrasonic detection of material damage without compromising the compressive, flexural or interlaminar shear strength of the ‘smart’ CFRP composite. In this work, CFRP samples including PZTs (G-specimens) were tested against plain samples (P-specimens), and their mean values of tensile strength and fatigue cycles to failure were found to be statistically the same (910 MPa and 713 000 cycles) using the one-way analysis of variance method. The same tests on P- and G-specimens with barely visible impact damage (BVID) showed that the corresponding group means were also the same (865 MPa and 675 000 cycles). Nonlinear ultrasonic experiments on impacted G-samples demonstrated that embedded PZTs could monitor the growth of BVID during fatigue testing, for a minimum of 480 000 cycles. This was achieved by calculating an increase of nearly two orders of magnitude in the ratio of second-to-fundamental harmonic amplitude. Finally, PZT transducers were confirmed functional under cyclic loading up to ∼70% of sample’s life, since their capacitance remained constant during ultrasonic testing.