Fiber Defects Research Articles

Observation of Visible Upconversion Luminescence of Soft Glass Multimode Fibers

This research investigates the visible upconversion luminescence which is induced by multiphoton absorption of soft glass fiber defects. The study of this phenomenon has thus far been restricted to standard silica fibers. We observed the emission of green and cyan light as a consequence of fiber material ionization. We investigate both the commercial ZBLAN step index and in-house-made tellurite nanostructured graded-index fibers. For the latter, the analysis of the luminescence signal permits us to determine the core and cladding refractive index difference. Upconversion luminescence is a powerful tool for characterizing soft glass fibers and a promising platform for innovative photonic technologies and mid-IR applications.

Advancing Carbon Fiber Composite Inspection: Deep Learning-Enabled Defect Localization and Sizing via 3-D U-Net Segmentation of Ultrasonic Data.

In nondestructive evaluation (NDE), accurately characterizing defects within components relies on accurate sizing and localization to evaluate the severity or criticality of defects. This study presents for the first time a deep learning (DL) methodology using 3-D U-Net to localize and size defects in carbon fiber reinforced polymer (CFRP) composites through volumetric segmentation of ultrasonic testing (UT) data. Using a previously developed approach, synthetic training data, closely representative of experimental data, was used for the automatic generation of ground truth segmentation masks. The model's performance was compared to the conventional amplitude 6 dB drop analysis method used in the industry against ultrasonic defect responses from 40 defects fabricated in CFRP components. The results showed good agreement with the 6 dB drop method for in-plane localization and excellent through-thickness localization, with mean absolute errors (MAEs) of 0.57 and 0.08 mm, respectively. Initial sizing results consistently oversized defects with a 55% higher mean average error than the 6 dB drop method. However, when a correction factor was applied to account for variation between the experimental and synthetic domains, the final sizing accuracy resulted in a 35% reduction in MAE compared to the 6 dB drop technique. By working with volumetric ultrasonic data (as opposed to 2-D images), this approach reduces preprocessing (such as signal gating) and allows for the generation of 3-D defect masks which can be used for the generation of computer-aided design files; greatly reducing the qualification reporting burden of NDE operators.

Dynamic micromechanical measurement of the flexural modulus of micrometre-sized diameter single natural fibres using a vibrating microcantilever technique

Abstract The dynamic response of a structure is a manifestation of its inherent characteristics, including material density, mechanical modulus, thermo- and viscoelastic properties, and geometric properties. Together, these factors influence how the material behaves in dynamic scenarios, dictating its damping properties and behaviour under varying forces. In this study we present a novel approach to accurately determine the flexural (bending) modulus of microscopic diameter natural fibres (flax) using microcantilever vibration analysis. Traditionally, the characterisation of the mechanical properties of fibres has relied on macroscopic methods such as tensile testing, which often results in high scatter in measurement data; furthermore, tensile testing does not accurately represent microscale or dynamic conditions and can be complex in terms of sample preparation and loading. To address this, we have developed a microscale technique involving the fabrication of microcantilevers using flat polypropylene support chips, inspired by microelectromechanical systems approaches. Our method provides a refined method for accurately characterising the mechanical modulus of flax fibres, with reduced data dispersion compared to traditional macroscopic testing. Furthermore, by reducing the influence of inherent fibre defects and maintaining homogeneity along the length of the fibre, our micro-scale technique provides reliable modulus determination. This work opens avenues for improved understanding and application of natural and man-made fibres, such as glass and optical fibres, in a variety of fields.

Quantitative test of delamination defects in CFRP with surface interference by laser thermography

Remote onsite inspection and quantitative evaluation of internal delamination defects in carbon fiber reinforced polymer (CFRP) structures is of great significance in practical engineering applications. In this paper, a new method for quantitative test and 3D imaging of internal delamination defects in CFRP with surface interference by long pulse laser thermography is developed. The variation of surface temperature field in a CFRP plate with both defect and surface interference irradiated by a uniform laser source was analyzed by simulation and experiment. Image processing of the collected laser thermography image sequence was carried out to obtain the size and shape of the possible defects. Meanwhile, a method with using the peak time of the temperature difference curve was developed to precisely evaluate the defect depth and eliminate the surface interference. The information was then combined to achieve 3D visualization imaging of the actual defects.

Fourier phase analysis combined with a fusion scheme in long pulse thermography

Fourier phase analysis (FPA) is a common routine in long pulse thermography (LPT). However, the change of appearance in maximum phase difference in the Fourier domain caused by different depths of defects results in low contrast images with fuzzy defect edges, limiting its application majorly. In this work, the detection capability of FPA is studied by modelling the image sequence length as a function of the defect depth, thereby quantifying the expected image sequence length for inspecting the defects at a specific depth and then, a strategy for selecting the optimal image sequence length is proposed by establishing the relationship between the maximum phase difference and the image sequence length. Followed by an image fusion procedure, the phase difference map with high-contrast defect edges can be obtained from the optimal image sequence. In addition, we show that the precooling treatment before detection is an effective way to improve the image quality for LPT. Experiments have been conducted in identifying defects in a typical glass fiber reinforced polymer (GFPR) with size of 200 × 150 × 6 mm3, where continuous thermal excitation for 100 s was provided by a pair of 1 kW halogen lamps and the images were recorded by an infrared camera with wavelength band of 8–14 μm. The resulting phase difference maps clearly present the edges of all defects with a signal-to-noise ratio greater than 43 dB, demonstrating the effectiveness of the proposed method in improving the image quality in LPT.

Connecting mechanical properties to hydrogen defects in PAN-based carbon fibers

Connecting mechanical properties to hydrogen defects in PAN-based carbon fibers

A novel microwave-based non-destructive testing system for detection of sub-surface defects in natural flax fiber reinforced composite material

In the work presented here, microwave non-destructive testing using a rectangular waveguide loaded with a novel planar unit cell filter for the inspection of defects in the composite is studied. The proposed planar filter is designed as a unit cell with a copper (annealed) microstrip component on an FR-4 substrate. The waveguide loaded with the planar filter exhibits a reflection coefficient of −35.919 dB at a frequency of 10.548 GHz. This planar filter waveguide is used as the sensor probe for the non-destructive testing method proposed here and is aimed to increase the sensitivity by measuring the differences in permittivity of materials. Computer simulation technology software (CST studio suite) is used for implementing the design, simulation, and material/defect evaluation. The defects studied are voids, cracks, and delamination in an epoxy-based natural flax fiber-reinforced composite material. The performance-improved microwave sensor probe is used to pick up permittivity variations in the presence of defects in lax-based natural fiber-reinforced composite material.

Research on internal defect detection of epoxy glass fiber composites based on terahertz imaging technology

Composite materials are characterized by light weight and high specific strength, which have been widely used in the field of transportation. The rapid and non-destructive detection of defects in the process of composite material production can be regarded as the key and guarantee for the safety. In this paper, the terahertz imaging technology was used to explore the detection of epoxy glass fiber composite samples with varied defects. Terahertz time-domain spectroscopy (THz-TDS) of different depths of defects in epoxy glass fiber samples were corrected by multiple pretreatment methods. Different variable selection methods were used to screen the spectral data. The Partial Least Squares (PLS) and Least Squares Support Vector Machines (LS-SVM) defect depth quantitative models were established, respectively. The visualization of the defect area is achieved by combining with different types of binary threshold segmentation methods. It was found that the ALS-SPA-LS-SVM model presents the best effects, and Rp and RMSEP of prediction set are 0.9998 and 0.0152 respectively. The grayscale histogram bimodal method has the best effect on detecting the defect area. It is feasible to detect the defects in epoxy glass fiber by THz-TDS combined with LS-SVM and image processing technology, which can achieve rapid and nondestructive detection of the location, depth and area of the defects. It provides the technical basis for defect detection in the process of composite material production. In this way, this study provides new methods and means for defect detection and quality supervision of other types of composite materials.

Novel continuous fiber bending experiment to determine size- and spatial-distribution of surface defects in glass fibers

Tensile strength models for unidirectional composites require scaling of fiber strength distributions to small length scales in the range of the fiber radius to the ineffective length (5–200 μm). No test methods currently exist to measure and validate fiber strength at these length scales. In this paper, a novel continuous fiber bending test method is developed to measure the size and spatial distribution of defects in S-glass fibers. For this test method, a single glass fiber was placed between Kapton film and tape, and the sample is subjected to a sequence of controlled radii of curvatures ranging from 350 μm to 25 μm in a confocal microscope. From the bending tests, the number of defects and spacings between the defects over 780 mm length of fiber are obtained for each level of flexural stress. The Kapton film confines the fiber fragments and maintains fiber alignment allowing the same sample to be tested at lower radius of curvature (higher flexural strain/stress). The associated defect size is calculated using fracture mechanics. These results are used to map the size- and spatial-distribution of critical defects on the tension surface of a glass fiber. A methodology is presented to map additional defects around the circumference of the fiber, while maintaining the size and spatial distribution obtained from the bending tests. The results show that the lognormal distribution best fits the spatial distribution of defects along the fiber axis. The results also indicate that extrapolating the Weibull strength distribution (be it bimodal or unimodal) to gage lengths less than 200 μm leads to serious errors in fiber strength prediction.

Surface integrity and acoustic emission characteristics during slot milling 3D carbon/carbon composites using superabrasive diamond grinding point

This study mainly investigated the effect of machining parameters on surface morphology and defects in three-dimensional carbon/carbon ceramic matrix composites during slotting with superabrasive diamond grinding points. Specifically, it discussed the mechanisms of burr and uncut fiber formation based on the relationship between grinding and fiber directions. The experimental work was conducted under different spindle speeds and feed speeds, revealing that the poor machining surfaces and severe defects occurred at the minimum spindle speed (2000 rpm) with maximum feed speed (160 mm/min), and most uncut fiber defects occurred when the fiber cutting angle exceeded 90°. Acoustic emission (AE) signals were analyzed via signal processing techniques to capture the energy and frequency characteristics of slotting process under various machining parameters. Results indicated that spindle speed had a significant impact on the frequency band distribution of AE signals, while feed speed primarily influenced the stability of signal profiles. The material removal and tool wear mechanisms were examined through SEM micrographs of machined surfaces and grinding points, revealing that fiber debonding and fracture were the primary material removal mechanisms, and were highly dependent on the fiber cutting angle. Abrasive particles with relatively lower cutting velocity at the center of the grinding tool suffered severe damage/wear.

Effect of Graphite Defect Type and Heterogeneous Atomic Doping Structure on the Electromagnetic Wave Absorption Properties of Sulfur-Doped Carbon Fibers

The heterogeneous atom-doped structure breaks the atomic-level symmetry of the local microstructure, and it and the defect design are considered to be an efficient way to fabricate microwave absorbers with a wide effective bandwidth and strong absorption capability. Herein, we have successfully designed and synthesized sulfur-doped carbon fibers by combining a strategy of introducing heterogeneous atoms with defect engineering. The analysis of Raman spectroscopy and transmission electron microscopy data reveals the correspondence between the type of graphite defects in sulfur-doped carbon fibers and their microwave absorption properties. The heterostructure formed by graphite and amorphous carbon can be considered a boundary-type defect, which is beneficial for microwave absorption. The introduction of vacancy defects and sulfur atom doping in graphite crystals breaks the symmetry of the graphite structure and leads to changes in dipole polarization. The ratio of the two sulfur-doped structures, C═S and C–S–C, is controlled to achieve the microwave absorption properties of the material in the high-frequency region. The three-dimensional structure formed by the cross-linking of one-dimensional fibers is more conducive to the formation of macroscopic eddy current losses. The strong absorption of microwaves at extremely thin thicknesses has been achieved with S-doped carbon fibers due to the synergy of multiple loss mechanisms. When the absorber thickness is only 0.98 mm, the optimized S-doped carbon fiber can reach a minimum reflection loss (RL) of −69.232 dB, and when the thickness is 1.04 mm, the widest effective bandwidth reaches 3.9 GHz (14.1–18 GHz), demonstrating a strong electromagnetic wave absorption capability.

Mitochondrial Dysfunction Links to Impaired Hippocampal Serotonin Release in a Mouse Model of Alzheimer's Disease.

Deprivation of extracellular serotonin has been linked to cognitive decline and neuropsychiatric disturbances in Alzheimer's disease (AD). However, despite degeneration of serotonin-producing neurons, whether serotonin release is affected in AD-sensitive brain regions is unknown. This study investigated the impact of mitochondrial dysfunction in decreased hippocampal serotonin release in AD amyloidosis mouse model 5xFAD mice. Electrochemical assays were applied to examine hippocampal serotonin release. We also employed multidisciplinary techniques to determine the role of oligomeric amyloid-β (Aβ) in hippocampal mitochondrial deficits and serotonin release deficiency. 5xFAD mice exhibited serotonin release decrease and relatively moderate downregulation of serotonergic fiber density as well as serotonin content in the hippocampal region. Further experiments showed an inhibitory effect of oligomeric amyloid-β (Aβ) on hippocampal serotonin release without affecting the density of serotonergic fibers. Pharmaceutical uncoupling of mitochondrial oxidative phosphorylation (OXPHOS) disrupted hippocampal serotonin release in an ex vivo setting. This echoes the mitochondrial defects in serotonergic fibers in 5xFAD mice and oligomeric Aβ-challenged primary serotonergic neuron cultures and implicates a link between mitochondrial dysfunction and serotonin transmission defects in AD-relevant pathological settings. The most parsimonious interpretation of our findings is that mitochondrial dysfunction is a phenotypic change of serotonergic neurons, which potentially plays a role in the development of serotonergic failure in AD-related conditions.

Improving Magnetic Field Response of Eddy Current Magneto-Optical Imaging for Defect Detection in Carbon Fiber Reinforced Polymers

A large number of carbon fiber reinforced polymers have been applied to aircraft and automobiles, and many nondestructive testing methods have been studied to detect their defects. Eddy current magneto-optical imaging nondestructive testing technology has been widely used in the detection of metal materials such as aircraft skin, but it usually requires a large excitation current and, at present, can only detect metal materials with high conductivity. In order to take full advantage of the innate benefits and efficiency of eddy current magneto-optic imaging and enable it to detect defects in carbon fiber reinforced polymers with weak conductivity, it is necessary to improve the magnetic field response of the eddy current magneto-optic imaging system and explore suitable excitation and detection methods. The scanning eddy current magneto-optical imaging nondestructive testing device built in this study has improved the magnetic field response of the system, and the eddy current magneto-optical phase imaging testing method has been proposed to detect the crack defects of carbon fiber reinforced polymers. The effectiveness of the method has been verified by simulation and experiment.

Effective X-ray micro computed tomography imaging of carbon fibre composites

Compression moulding of carbon fibre sheet moulding compounds is an attractive manufacturing method for composite structures. Investigating fibre orientation, defects and voids in these components is important for quality control. X-ray computed tomography is a non-destructive imaging method used on different kind of sheet moulded compound to identify such issues, but it is still a challenge on carbon fibre sheet moulding compound due to the similarities in density of the carbon fibres and polymer matrix. This study aims to determine the best-practice for optimising acquisition parameters for imaging carbon fibre composites. The first experiment assessed the effect of excess material on a region of interest scans was investigated, a common acquisition approach to maintain resolution to resolve fibres. This showed in this specific case the scan quality decreases when surrounding material reaches approximately 75% of the field of view indicating region of interest scanning is feasible. In the second experiment seven X-ray computed tomography parameters were systematically evaluated to optimise image quality for observing the structures and defects, resulting in 168 scans. The results indicate that the source–detector distance and the source voltage have the most significant impact, where users should always consider maximising this distance and minimising voltage for the best image quality.

Measurement of microfibril angle in plant fibres: Comparison between X-ray diffraction, second harmonic generation and transmission ellipsometry microscopies

The orientation of cellulose microfibrils within plant fibres is one of the main factors influencing their mechanical properties. As plant fibres are more and more used as reinforcement for agro-composites, their mechanical properties have a strong influence on the final composite properties. It is, therefore, of interest to obtain reliable information about the microfibril angle (MFA) to better support the choice of fibres depending on the product requirements. In the present study, the reliability and specificities of three non-destructive methods that allow analysis on the same fibre glued on a holder; X-ray diffraction (XRD), second harmonic generation (SHG) and transmission ellipsometry (TE) microscopy; are investigated. Three types of plant fibres, with both low (nettle), and high (cotton, sisal) MFA values, are compared and their geometry and biochemical composition are characterised. The results obtained on the same fibre confirm that MFA analysis remains tedious and that despite their limitations, the methods are complementary depending on the information requested. Indeed, SHG is recommended for direct, qualitative and plane-selective mapping of heterogeneities in macrofibril orientations at various depths. However, reliable quantitative results with SHG depend on the initial image quality and could benefit from further image processing refinement. On the contrary, XRD and TE measure MFAs over the entire fibre thickness and provide variations along the fibres if a sufficient optical/spatial resolution is reached. Regarding the characterization of intrinsic defects in plant fibres, both SHG and TE suffer from uncertainties induced by the disorganization of the microfibril network and the lack of symmetry between the front and back fibre walls. Finally, all techniques prove to be dependant on the initial fibre alignment and geometry (i.e. twisting, double fibre configuration or form factor) which vary along the fibre length and should be carefully taken into account.

Aggregation of ODC(I) and POL Defects in Bismuth Doped Silica Fiber

First-principles calculations were used to simulate the aggregation of the peroxy chain defect POL and the oxygen vacancy defect ODC(I). Defect aggregation's electronic structure and optical properties were investigated. The two defects were most likely to accumulate on a 6-membered ring in ortho-position. When the two defects are aggregated, it is discovered that 0.75 ev absorption peaks appear in the near-infrared band, which may be brought on by the addition of oxygen vacancy defect ODC(I). We can draw the conclusion that the absorption peak of the aggregation defect of ODC(I) defect and POL is more prominent in the near infrared region and visible light area than ODC(I) defect and POL defect.

An efficient parametrized optical infrared thermography 3D finite element framework for computer vision applications

Matching infrared thermography (IRT) with deep learning-based computer vision has recently gained a lot of interest for automated defect assessment in materials. One of the remaining bottlenecks concerns the necessity of a large and diverse experimental and/or virtual training dataset in order to achieve a sufficiently generalizable computer vision algorithm. This paper presents a parametrized 3D finite element (FE) framework, implemented in Fortran90, for efficiently simulating optical infrared thermographic inspection of multi-layer anisotropic media and establishing large-scale virtual dataset with sufficient diversity. The interface element is introduced for the modelling of an imperfect thermal contact, allowing to simulate a variety of defect types. The flexibility of the interface element makes it possible to simulate delaminations with different thickness using the same discretized model. Validation is done for two benchmark cases which are representative for a fiber reinforced polymer laminate with delamination-like defects. In order to achieve true-to-nature thermographic simulation data, non-uniform heating conditions are adopted from experiment, and a stochastic morphology generator is introduced for modelling realistic irregular defect geometries. To demonstrate the added value of a large, diverse and true-to-nature virtual database for computer vision applications, a Faster-RCNN model was trained on a generated virtual dataset for the detection of delamination-like defects in fiber reinforced polymer laminates. Application of the trained Faster-RCNN on experimental thermographic data yields excellent inference results, illustrating the high generalization ability of the virtually trained object detector.

Enhanced thermographic inspection of woven fabric composites by [formula omitted]-space filtering

Infrared thermography is a well-known non-destructive testing technique for detecting defects in unidirectional fiber reinforced polymer laminates. When applied to woven fabric reinforced polymers, however, the weave structure causes strong disturbances and patterns in the background of the thermal images, making accurate and reliable defect assessment a challenging task. In this paper, the concept of k-space filtering is introduced for an improved evaluation of thermographic images obtained from woven fabric composites. An algorithm is introduced to automatically decompose a thermographic image into an image which contains the structured thermal background related to the weave pattern, and a residual image representing other features, e.g. defects. The proposed k-space filtering approach is demonstrated on thermographic data from various woven fabric composites with different weave patterns and defect scenarios, clearly showing an enhanced performance in terms of defect detection and sizing.

Recognition of drilling-induced defects in Fiber Reinforced Polymers using Machine Learning

The machining of Fiber Reinforced Polymers (FRP) is accompanied by specific defects such as delamination, uncut fibers, and others. Such defects are unique in their shape and size for different FRP types, used tools, and applied cutting conditions. Therefore, defect recognition and quantification remain a central challenge in the quality control of FRP components from an accuracy and time balance perspective. The study presents the implementation of Machine Learning techniques for automated recognition of the hole defects resulting from drilling Flax/PLA biocomposites using HSS drills with various cutting data. The paper discusses the effectiveness and stability of the developed solution.

Automatic Detection of CFRP Subsurface Defects via Thermal Signals in Long Pulse and Lock-In Thermography

Thermography is widely used to detect delamination defects in carbon fiber reinforced plastics (CFRP). This paper proposes a model to detect defects automatically by extracting the thermal signal characteristics of CFRP materials. An optically excited thermography system is constructed for pulsed and lock-in thermography experiments to compare thermal signal data sets in different excitation modes. A multi-task joint loss function is defined to train the model for defect detection and depth prediction. The effects of different attention modules (AM) are analyzed to improve the model performance. By comparing the effects of traditional thermography processing methods and methods based on Convolutional Neural Network (CNN), it is found that the proposed model can detect defects with minimum aspect ratio (ratio of short side to depth) of 2.5, and the relative error percentage in depth prediction is below 10%.