Images Of Composites Research Articles

Nito “Core” Fiber-Reinforced Epoxy Composites: Enhancing Properties with NaHCO<sub>3</sub> Treatment

This study explores the use of sodium bicarbonate-treated Nito core fiber as a natural and eco-friendly alternative for fiber-reinforced composites to address the challenge of enhancing the mechanical properties of composite materials while also prioritizing environmental sustainability. Nito core fibers were treated with different concentrations of sodium bicarbonate, an economical and eco-friendly alternative to alkali treatment, to enhance its compatibility with various matrices. FTIR results showed that NaHCO3 treatment effectively removed and reduced some non-cellulosic components present in the Nito fiber such as hemicellulose and lignin. This resulted in the NaHCO3-treated fiber-epoxy composite showing better tensile strength and modulus of elasticity than the epoxy composite reinforced with untreated Nito fiber. The use of treated fiber, however, did not have a noticeable effect on the flexural strength and flexural modulus of the epoxy composite. The SEM images of the nito fiber-epoxy composites showed better fiber-matrix adhesion between the treated nito fiber and epoxy matrix. Thermogravimetric analysis (TGA) of nito fiber-epoxy composites shows that the thermal stability of the composite is mainly due to the presence of cellulose, which can also be enhanced by some lignin. This study, therefore demonstrates the potential of Nito ‘core’ fibers as a viable substitute for synthetic reinforcements that can contribute to the advancement of composite material technology that aligns with the global shift towards environmentally responsible manufacturing practices.

Three-Dimensional Nanoscale Imaging of SiO2 Nanofiller in Styrene-Butadiene Rubber with High-Resolution and High-Sensitivity Ptychographic X-ray Computed Tomography.

SiO2 aggregates in styrene-butadiene rubber (SBR) were observed using ptychographic X-ray computed tomography (PXCT). The rubber composites were illuminated with X-rays focused by total reflection focusing mirrors, and the ptychographic diffraction patterns were collected using a CITIUS detector in the range of -75° to +75° angle of incidence. The projection images of the rubber composites were reconstructed with a two-dimensional resolution of 76 nm, and no significant structural changes were observed during the PXCT measurements. A three-dimensional image of the rubber composite was reconstructed with an isotropic resolution of 98 nm. Segmentation of SiO2 from the SBR, based on a histogram analysis of the phase shift, revealed a fragmented network structure of interconnected SiO2 aggregates.

Carbon fibre detection in polymer composites reinforced by chopped carbon fibres through digital image processing and machine learning

Mechanical and thermodynamical properties and thus machinability of carbon fibre reinforced polymer composites significantly depend on the fibre orientation relative to the load direction. However, the orientations of the fibre groups in polymer composites reinforced by chopped carbon fibres are stochastic; therefore, the properties and machinability of such composites are challenging to plan, predict and optimise. We developed four different and novel approaches for fibre detection in polymer composites reinforced by chopped carbon fibres: (i) detecting the fibres through naked eye supported manual drawing, (ii) digital image processing of optical images, (iii) machine learning-based fibre detection, and (iv) rectangle fitting on the outputs of the automated processes using the Chaudhuri and Samal method. The applicability of the novel approaches was tested through optically captured images of polymer composites reinforced by chopped carbon fibres. The developed methods are each capable of detecting fibre groups at the top and bottom of the composite plate with certain limitations. The rectangle fitting approaches performed the best from the point of view of correctly identifying of fibre groups, followed by the machine learning-based and the conventional digital image processed, respectively. As a result of this study, the machining process planning and condition monitoring of polymer composites reinforced by chopped carbon fibres is more deeply supported.

The Amazon River plume—a Lagrangian view

AbstractHydrographic data, nutrient data and bulk rates of nitrate uptake and primary production were determined in the Amazon River plume (ARP) in the Western Tropical North Atlantic (WTNA) during three cruises in May 2018, June/July 2019, with RV Endeavor and April/May 2021 with RV Meteor. Using daily quasi‐geostrophic surface velocity data from satellite observations, the geographical positions of the stations of observations were transformed onto Lagrangian coordinates to obtain a dynamically coherent and consistent spatial distribution. After the transformation, the observed surface salinity and temperature fields were consistent with the flow fields, the ARP formed a coherent structure and the retroflection of the North Brazil Current became visible. By transforming other surface variables such as nitrate concentration, photosynthetically available radiation, turbidity, bulk rates of nitrate uptake, and primary production onto Lagrangian coordinates, patterns became consistent with the physical variables at the surface. The use of “synchronous” fields as done here by transformation onto Lagrangian coordinates is essential for spatially structured analyses of data collected over tens of days in a highly dynamic region characterized by complex flow fields with low persistence such as the WTNA. Therefore, the use of the Lagrangian method provides a powerful tool for exploring spatial distributions of biologically relevant factors in regions with complex and dynamic flow patterns. These spatial distributions are qualitatively in agreement with satellite images of daily sea surface temperature and composites of monthly mean Chlorophyll a distributions.

An efficient finite element mesh generation methodology based on μCT images of multi-layer woven composites

High-fidelity models are essential for accurate finite element (FE) simulations of composite material behavior. This paper proposes an efficient meshing methodology based on micro-Computed Tomography (μCT) images. U-Net convolutional neural network was used for image segmentation. Connected yarns were then separated using an improved procedure based on watershed algorithm and geometric transformations. The proposed Constrained Delaunay-Advancing Front Technique (CD-AFT) surface reconstruction algorithm extracts point cloud of yarns from segmented images and outputs high-quality and smooth orientable manifold watertight triangulated surface. Intersecting meshes of yarns are separated through node position detection and Laplacian moving. Experimental results show that proposed methodology is capable of accomplishing mesh generation for different mesh sizes. Compared with commercial software, it has obvious advantages in mesh quality and size control. Since the proposed method operates independently of commercial software and manual operation, it facilitates the automated generation of numerous high-fidelity models from μCT images for FE simulations.

Hyperparameter tuning for deep learning semantic image segmentation of micro computed tomography scanned fiber-reinforced composites

Image segmentation with deep learning models has significantly improved the accuracy of the pixel-wise labeling of scientific imaging which is critical for many quantitative image analyses. This has been feasible through U-Net and related architecture convolutional neural network models. Although the adoption of these models has been widespread, their training data pool and hyperparameters have been mostly determined by educated guesses through trial and error. In this study, we present observations of how training data volume, data augmentation, and patch size affect deep learning performance within a limited data set. Here we study U-Net model training on four different samples of x-ray CT images of fiber-reinforced composites. Because the training process is not deterministic, we relied on seven-fold replication of each experimental condition to avoid under-sampling and observe model training variance. Unsurprisingly, we find greater training data volume strongly benefits individual models’ final accuracy and learning speed while depressing variance among replicates. Importantly, data augmentation has a profound benefit to model performance, especially in cases with a low abundance of ground truth, and we conclude that high coefficients of data augmentation should be used in scientific imaging semantic segmentation models. Future work to describe and measure image complexity is warranted and likely to ultimately guide researchers on the minimum required training data volume for particular scientific imaging deep learning tasks.

Modification of amino functionalized silica nanoparticles with L-proline and furanacrylic acid as novel composites for the efficient removal of methyl orange

The present study deals with the preparation of new composites in which L-proline (LP) and furnacrylic acid (FA) have been grafted on surface of amino functionalized silica nanoparticles (AFS). At first, AFS were prepared by sol-gel approach using tetraethoxysilane (TEOS) as silica source and aminopropyltriethoxysilane (APTES) as silica modifiers. Taking the advantage of amino group on AFS surface, LP and FA were easily grafted on silica surface to obtain LP grafted silica (AFS-LP) and FA grafted silica (AFS-FA) nanocomposites by post modification approach. As prepared composites were characterized by FTIR, SEM, XRD and TGA. The evidence of LP and FA on AFS was elucidated from the FTIR bands found at 1650–1698 cm−1 related to carbonyl group CO) of amide. Additionally, peaks observed at 2965 cm−1 (C-H stretching) of propyl groups and at 1050 cm−1 (Si-O-Si) indicated that the backbone of the material is preserved as silica. The micro images of SEM depicted that the AFS are spherical in shape and have more degree of monodispersivity whereas the SEM images of composites (AFS-LP and AFS-FA) have larger particle size and less degree of monodispersivity. Likewise, XRD peaks at 2θ (∼12°) revealed the mesoporosity of silica as well as hybrid silica and further depression in this peak in case of AFS-LP and AFS-FA depicted the existence of organic moieties on silica. TGA analysis depicted three decomposition steps (high weight loss) in case of AFS-LP and AFS-FA in contrast to two decomposition steps observed in case of AFS. The higher weight loss observed in the case of hybrid composites depicted the grafting of organic molecules on AFS. The prepared composites AFS-LP and AFS-FA were deployed to investigate their adsorption capacity to remove methyl orange (MO) from aqueous media. Experiments revealed that the composites have high adsorption capacity in acidic media (pH=2) with % removal of 85.05 % for AFS-LP and 81.70 % for AFS-FA. The experimental data was evaluated for adsorption isotherms and value of correlation factor (R2) was close to unity in case of Freundlich representing that the adsorption follows Freundlich isotherm. The data was also evaluated for kinetic models and higher values of R2 as well as closeness of experimental and calculated values of Qe predicted that adsorption of MO followed pseudo-second order kinetic model.

Investigation on the elimination of striations in NDE of glass fiber composites

Glass fiber composites are widely used due to their unique performance advantages. However, defects in glass fiber composites can significantly affect their performance, making nondestructive evaluation necessary. During the X-ray spectral imaging of glass fiber composites, the 0/90° cross-stacking mode of glass fiber cloth results in irregular horizontal and vertical stripe noise, which seriously reduces the clarity of defect detection. Therefore, an appropriate algorithm is required to remove the noise. In this study, by analyzing the Fourier spectrum of X-ray spectral images of glass fiber composites, it was found that the stripe information is concealed in the vertical and horizontal bright lines in the middle of the Fourier spectrum image. A novel cross-sector filter was designed, and a stripe noise removal algorithm based on the cross-sector filter was proposed. The degree of the filter’s central angle can be adjusted according to the distribution of the stripe noise in the frequency domain, which removes the noise without losing much useful image information.

Deep-learning versus greyscale segmentation of voids in X-ray computed tomography images of filament-wound composites

Filament-wound composites (FWC) are prone to high void contents, with large and complex-shape voids. It is critical to characterise these voids accurately to understand their effect on part strength. The characterization depends on the accuracy of the analysis technique, for example X-ray computed tomography and the subsequent void segmentation. This paper compares conventional greyscale thresholding to deep-learning (DL) based segmentation. The processing steps for both techniques are discussed. The greyscale thresholding contains segmentation errors due to the simple one-parameter algorithm and the pre-processing operations required for segmentation. This reduces the accuracy of void characterisation. The DL-based segmentation is found to be more accurate for characterisation of void size, shape, and location. The processing-time and system requirements are discussed, helping to determine the suitable segmentation technique based on desired results.

Effect of Nano-filler on the Manufacturing and Properties of Natural Fiber-based Composites: A Review

Natural fiber reinforced polymer composite offers ecological safety towards a sustainable environment. Meanwhile, the deficiency of the poor interfacial bonding between fiber and matrix draws the attention of researchers to be sorted out. The use of inorganic nanofiller is considered as a possible solution to overcome the hurdle nowadays besides strengthening the composite properties. This article thoroughly reviews the use of inorganic nanofillers in natural fiber composites, covering different manufacturing processes and properties. Factors of various manufacturing techniques occupied for composite fabrication are investigated. Moreover, the influences of different nanofillers on mechanical, thermal, chemical, and physical properties of composites are discussed. In addition, Scanning Electron Microscopy (SEM) images of the bio composites are critically reviewed that usually exhibit the interfacial bonding and the fractures of the specimen. Furthermore, application of such natural fiber composites and the future investigation pathway in using inorganic nanofiller in composite are narrated.

Contact electrification of porous PDMS-nickel ferrite composites for effective energy harvesting

Energy harvesting technologies are becoming popular owing to their usage in the operation of low-power consumer electronics and as an alternative power source. Specifically, triboelectric nanogenerators (TENGs) have drawn much attention as they can efficiently scavenge waste mechanical energy into electrical output. Careful material selections can further improve the performance of TENGs. In this work, a spinel ferrite material with the chemical formula NiFe2O4 (NFO abbreviated further) is synthesized using a solid-state reaction route. The structural and magnetic property of the NFO has been studied, showing the cubic symmetry and ferromagnetic nature of the sample. The 3D tomography images of the PDMS-NFO composites were carried out using X-ray tomographic microscopy. To enhance the performance of the TENGs, we adopted a porous media by evaporation of water during the curing process of PDMS-NFO composites. A single-electrode operating mode was adopted for TENG fabrication. The electrical response of the device was carried out using different wt% and frequencies. The 12 wt% of NFO in PDMS (PN12 device) delivered a voltage, current, and charge of 60 V, 300 nA, and 34 nC, respectively. The charge density of the plain and porous composite-based TENG was compared to confirm the enhancement of the charge produced on the triboelectric layers. The commercial capacitors and energy harvesting based on a smart home were demonstrated using the TENG devices.

Automatic segmentation and fibre orientation estimation from low resolution X-ray computed tomography images of 3D woven composites

In this work, we compare different methods for the automatic segmentation of microtomographic images at the mesoscopic scale of 3D woven carbon fibre composite materials. We use a dense neural network approach fed with features based on the monogenic signal and features learned by a convolutional neural network. To determine local fibre orientations in the composite, we also propose to use the monogenic signal with a structure tensor approach. The performance of the segmentation methods is compared on a first sample for which a reference solution is given. To demonstrate the global capability of the method, the neural networks are then used to segment a second similar sample, for which there is no reference solution. A transfer learning strategy is proposed to take advantage of the knowledge learned on the first sample and to limit the interaction with the user. This simply consists of re-learning only the decision layers, thus limiting the number of examples required. Finally, the best result is obtained with a combination of the signal-based approach and the learning-based approach by merging the monogenic and convolutional neural network features.

Fiber Orientation Reconstruction from SEM Images of Fiber-Reinforced Composites

The orientation of fibers in composites reinforced with short fibers can provide insight into the microstructure of the material and considerably affect its macroscopic characteristics. However, the present standard techniques for detecting fiber orientation and length based on microscopic image processing have faults in practical applications, including high effort, low efficiency, and unreliable measurement results. In this study, a method for measuring fiber orientation based on 3D reconstructions of scanning electron microscope (SEM) images is provided. The geodesic active contour (GAC) model is applied to segment the fibers in the SEM images. Matching the fiber contours with the scale-invariant feature transformation (SIFT) algorithm successfully extracts 3D orientation information from the fiber contours. The unit vector of the fiber axis is fitted from the extracted point cloud using the ordinary least squares (OLS) method. With a maximum deviation of 3.83° and an average deviation of 1.50°, the measurement findings of this method are substantially comparable to those of the image-measuring instrument. This paper offers a quantitative approach to studying the microstructure of short fiber-reinforced composites, thereby furnishing objective evidence to support the development and research of such materials.

Microstructure, Mechanical and Thermal Properties of Al/Cu/SiC Laminated Composites, Fabricated by the ARB and CARB Processes

The aim of the current work is to investigate the effect of SiC particle weight percent and rolling passes on Al/Cu/SiC laminated composites, fabricated by accumulative roll-bonding (ARB) and cross-accumulative roll-bonding (CARB) processes. The optical microscopy (OM) images of composites revealed that despite the good bonding of the layers, they underwent plastic instabilities as a consequence of strain hardening of the layers. However, these instabilities occurred more in ARBed composites than in composites fabricated by the CARB process. This is because in the latter process, the composites are rolled in two directions, which leads to better strain distribution. Furthermore, with an increase in passes, SiC particles were well distributed in the matrix and interfaces. The mechanical findings showed that, by increasing passes, there was a growth in the values of strengths and elongation. This behavior is believed to be related to increased work-hardening of layers, better distribution of reinforcing particles, and an enhanced bonding of interfaces at higher rolling passes. In addition, the results of thermal conductivities showed a downward trend with an increase in passes; in fact, the increased number of Al/Cu interfaces declined the heat conduction of composites.

Synthesis and Characterization of 𝒌-Carrageenan/PVA Nanocomposite Hydrogels in Combination with MgZnO Nanoparticles to Evaluate the Catechin Release

In the current study, nanocomposites were prepared by combining k-carrageenan, polyvinyl alcohol (PVA), and doped nanoparticles (Magnesium oxide) MgO, (Magnesium Zinc oxide) MgZnO 1%, MgZnO 3%, and MgZnO 5%. The nanoparticles were synthesized by a sol-gel method and mixed with a mixture of k-carrageenan/PVA (Ca/PVA) in various ratios. The structure of the composites was analyzed using thermogravimetric analysis (TGA), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). The Ca/PVA mixture was then mixed with nanoparticles and loaded with active ingredient, catechin. Scanning electron microscope (SEM) and texture analysis were performed to analyze the nanocomposites. Entrapment efficiency (EE%) and drug release studies confirmed that k-carrageenan/PVA/MgZnO 5% had the highest EE% at 81.58% and a drug release of 75.21% ± 0.94. The EE% of k-carrageenan/PVA/MgO was 55.21% and its drug release was 45%. This indicates that ZnO plays an effective role in the structure and performance of Ca/PVA composites. The SEM images of MgO composites show smoother surfaces compared to MgZnO composites. This may be one of the reasons for the increased EE% and drug release of MgZnO composites. The addition of ZnO to the composite structure can lead to the appearance of pores on the surface of the composite, increasing entrapment and drug release.

Delamination measurement in glass fibre reinforced polymer (GFRP) composite based on image differencing

Drilling-induced delamination is the most severe material defect in fibrous polymeric composites. Although many delamination measurement methods are available, the outputs of recent digital image processing (DIP) based delamination measurement methods are significantly affected by the non-drilling induced material defects and anomalies like cavities or dirty textured surfaces. Therefore, the main aim of the present research was to develop a method capable of drilling-induced delamination measurement in fibrous polymeric composites based on the image differentiation principle. In an attempt to implement this principle, images of the undrilled composites were referenced/compared with the images of the drilled composites. The proposed delamination measurement method is tested through mechanical drilling experiments in glass fibre reinforced polymer (GFRP) composites and compared with conventional DIP-supported manual measurements. The experimental results show a good agreement between the delamination detected by the naked eye, the conventional and the proposed method. Although the proposed method slightly increases the drilling operation time, the comparative differentiating makes it a good solution to detect drilling-induced material deformations in textured and semi-specular surfaces. The proposed method can separate drilling-induced delamination from other non-drilling-induced material discontinuities or anomalies. Considering the automated, reproducible and complex nature of the novel method, it may replace current qualification operations in high-end industries.

Cationic Copolymers of Norbornylized Seed Oils for Fiber-Reinforced Composite Applications.

Norbornylized seed oils, i.e., norbornylized linseed oil (NLO), norbornylized soybean oil (NSO), and norbornylized high oleic soybean oil (NHOSO), were synthesized via the Diels–Alder reaction of seed oil and dicyclopentadiene (DCPD) at high temperature (∼235 °C) and high pressure (∼80 psi), followed by cationic copolymerization using DCPD with boron trifluoride diethyl etherate catalyst. Norbornylized seed oils were characterized using H1 nuclear magnetic resonance (NMR), attenuated total reflectance-Fourier transform infrared, and gel permeation chromatography (GPC). Copolymers were formulated with four different DCPD contents, and curing was investigated using dynamic differential scanning calorimetry (DSC) measurements. It was found that the curing followed NLO > NSO > NHOSO with NLO having the highest exotherm, lowest activation energy, and lowest onset temperature. Furthermore, the gelation times were the least for NLO-DCPD copolymers. As anticipated, the degree of unsaturation and norbornene moieties strongly influenced the curing of copolymer thermosets. The copolymer products were compression-molded into thermosets and characterized by DSC, Soxhlet extraction, thermogravimetric analysis (TGA), H1 NMR, solid-state C13 NMR, and GPC. NLO-DCPD thermosets demonstrated high cure, higher thermal stability, glass transition temperature, and cross-linking capability compared to the other seed oil-DCPD counterparts. NMR and GPC results further suggested that bis-allylic and norbornene units concomitantly participated very actively during the cationic curing reaction. Moreover, scanning electron microscopy images of glass fiber-reinforced NLO-DCPD copolymer composites demonstrated good interfacial adhesion between the polymer matrix and fiber phases, imparting enhanced thermo-mechanical properties. This research opens a new venue for higher biobased greener polymer constituent for composite applications.

Hybridization effect on the mechanical properties of basalt fiber reinforced ZnO modified epoxy composites

AbstractThis paper discusses the fabrication of nano ZnO filled epoxy/basalt fiber composites and investigates the effect of such hybridization on the mechanical properties of the composites. The epoxy resin is modified by the addition of ZnO nanofiller in different wt.% (1–5 wt%). The dispersion of the nanofiller within the epoxy resin is brought about by sonication technique. The composites are fabricated through wet layup followed by curing under compression molding. To ascertain the effect of resin modification, flexural and tensile strengths are evaluated. The inter‐laminar shear strength (ILSS) is determined to identify the compatibility of the ZnO nanofiller with the epoxy and the basalt fibers. The improvement in the ILSS indicates thorough wettability being achieved between all the composite components. The addition of ZnO nanofiller improved the flexural strength, while there was no remarkable improvement in the tensile strength of the ZnO modified epoxy/baslat (BEZ) composite. Maximum improvement in the strengths is observed for BEZ2 composite with 2 wt% of ZnO nanofiller. For BEZ2 composite, the flexural and tensile strength increased by about 22% and 9.78% respectively in comparison to unfilled BEZ0 composite. The ILSS for BEZ2 composite improved by 21.74% in comparison to BEZ0 composite. In addition, more than 2 wt% addition of the ZnO nanofiller resulted in the reduction of flexural, tensile and ILSS of the composite due to agglomeration. Such agglomeration resulted in the failure of the composite due to delamination, matrix cracking and fiber fracture as observed through scanning electron microscopy images of the fractured composites.

Utilization of Mucuna atropurpurea stem fiber as a reinforcement in fiber reinforced plastics

AbstractThis research investigated the scope of using a fiber harvested from Mucuna atropurpurea (MAF) plant as reinforcement in fiber‐reinforced plastics. An optical microscope and a pycnometer were used to calculate the diameter (289 ± 21 μm) and density (1082 ± 29 kg/m3) of the MAF. Using chemical compositional analysis, 58.74 ± 5.74 wt% cellulose and 16.31 ± 3.21 wt% hemicellulose, and 14.22 ± 3.36 wt% lignin and 0.38 ± 0.08 wt% wax components were found in the MAF. Numerous chemical stretching in the MAF were checked using Fourier transform infrared spectroscopy. Briodo analysis (68.08 kJ/mol) showed the kinetic activation energy of the MAF. Surface morphological inspections suggested surface treatments of MAF due to contaminations and wax on the fiber surface. The statistical analysis conducted for determining the tensile properties of MAF revealed that MAF is a suitable candidate to produce fiber‐reinforced plastics. Characterization of MAF‐reinforced polyester composites showed that 40 mm fiber length and 30 wt% fiber enhanced tensile strength (109.51 ± 2.1 MPa), flexural strength (156.62 ± 3.1 MPa), and hardness (94 ± 4 HRRW). Fractography images of composites with fiber pull‐outs in the MAF‐reinforced plastics were taken for analysis. Less cavities and voids in the 30 wt% MAF‐reinforced composites were found along with reduced water absorption.

Solar power harvesting with metal modified windshield glass to obtain hot water

Energy solar harvesting through low environmental impact resources is an underlying objective for future technological developments. In this work, an innovative design of a vitro-metallic composite material is developed with Cu0, Zn0 and ZnO microparticles dispersed in a glass-ceramic matrix. The uppermost feature of these materials is that they are made of the reused of vehicle windshield glass disposed. The vitreous composite performance is evaluated in different seasons and under diverse weather conditions. The energy absorption is analyzed from the results of the UV–vis spectra and SEM images of the different composites. In this work it is demonstrated that the studied material has a good performance even in low temperature regions, reaching an efficiency between 30 to 50 [KJ/ KgH2O] with a 0.3% wt of metal-modified glass-ceramic material in water. The material response was analyzed through the composite material UV–vis spectrum. The new glass-ceramic materials are efficient in the process of heating water without the need of expensive devices or other forms of energy applications.