Composite Face Research Articles

Investigation of Free Vibration Behavior for Composite Sandwich Beams with a Composite Honeycomb Core

The purpose of the current research is to determine the effect of fiber type, volume ratio, and matrix type on the vibration properties of sandwich beams made of composite face sheet and core. The typical sandwich structure consists of three layers: face sheets, core, and adhesive bonding, and in this research, the adhesive layer between the face sheets and core was abolished by preparing the overall mold with fibers inside and casting the resin to fill the face sheet and core parts. The face sheets of the composite beams are made from a polyester or epoxy matrix reinforced with glass fiber, carbon fiber, and hybrid fiber, and the core is a honeycomb consisting of random glass fibers immersed in a resin matrix (polyester or epoxy). 22 composite sandwich beams were constructed to conduct vibration testing. The vibration results obtained experimentally were compared to the ANSYS R1 2022 software, and the results were in very good agreement. Hybrid fibers and polyester matrix achieved the highest values of natural frequency for (clamped-clamped) boundary conditions, where the natural frequency value of the hybrid fiber and polyester matrix reached (2037 Hz) at a volume fraction of (23.14%) experimentally and the natural frequency reached (1804.5 Hz) at a volume fraction of (21.95%) experimentally for the simply supported boundary condition.

Forensic sketch-to-photo transformation with improved Generative Adversarial Network (GAN)

Forensic sketch-to-photo transformation is a critical technique in criminal investigations in which forensic artists detail their drawings based on eyewitness descriptions. However, sketch-to-photo traditional methods are not an exception to the usual differences between sketch detail and photograph style. This system aims at improving the accuracy and realism of sketch-to photo transformation through an improved Generative Adversarial Network. The system aims to leverage advanced GAN architectures to bridge the gap between sketches and photos by learning intricate mappings and stylistic nuances. The GAN model comprises two neural networks: a generator and a discriminator. The generator synthesizes photo-realistic images from forensic sketches while the discriminator evaluates the authenticity of the generated images, iteratively refining the generator's output.

Using Artificial Neural Networks to Predict the Bending Behavior of Composite Sandwich Structures

The refinement of effective data generation methods has led to a growing interest in using artificial neural networks (ANNs) to solve modeling problems related to mechanical structures. This study investigates the modeling of composite sandwich structures, i.e., structures made up of two laminated composite face sheets sandwiching a lightweight honeycomb core. An ANN was utilized to predict structural deflection and face sheet stress with low computational cost. Initially, a three-point load mode was used to determine the flexural behavior of the composite sandwich structure before subsequently analyzing the sandwich structure using the Monte Carlo sampling tool. Various combinations of face sheet materials, face sheet layer numbers, core types, core thicknesses and load magnitudes were considered as design variables in data generation. The generated data were used to train a neural network. Subsequently, the predictions of the trained ANN were compared with the outcomes of a finite element model (FEM), and the comparison was extended to real structures by conducting experimental tests. A woven carbon-fiber-reinforced polymer (WCFRP) with a Nomex honeycomb core was tested to validate the ANN predictions. The predictions from the elaborated ANN model closely matched the FEM and experimental results. Therefore, this method offers a low-computational-cost technique for designing and optimizing sandwich structures in various engineering applications.

Low‐Velocity Impact Behavior of 3D‐Printed Sandwich Panels with Integrated Composite Face Sheets

Low‐Velocity Impact Behavior of 3D‐Printed Sandwich Panels with Integrated Composite Face Sheets

Free vibration analysis of sandwich cylindrical shells with functionally graded carbon nanotube-reinforced composite face sheets using the differential quadrature (DQ) method

Free vibration analysis of sandwich cylindrical shells with functionally graded carbon nanotube-reinforced composite face sheets using the differential quadrature (DQ) method

Numerical and experimental approach on vibration characterization of non-uniform hybrid sandwich plates

In this paper, the free vibrational response of various non-uniform composite sandwich plates with different hybrid honeycomb core types and varied weight percentages of multi-walled carbon nanotube (MWCNT) is analyzed experimentally and numerically. The governing differential equations of motion of the various honeycomb non-uniform composite sandwich plates are derived using higher order shear deformation theory and numerically solved. The various non-uniform fiber-reinforced composite face plate configurations were fabricated using the vacuum-assisted hand lay-up technique with the ply-dropping off at different domains to achieve different taper configurations. The honeycomb core was fabricated using a corrugated die, and hand lay-up method to yield various configurations. In the corrugated honeycomb core, apart from the standard hexagonal shape, composite laminated strips were longitudinally reinforced between the corrugated shape continuously and intermediately to increase the strength and damping property of the structure. The various weight percentages of R-COOH functionalized MWCNT were distributed homogeneously into epoxy using the titanium probe-assisted ultra-sonication method. Material properties such as Young’s modulus and Poison’s ratio, were evaluated for the face plate using ASTM E1876. An alternative dynamic approach was carried out to obtain the shear moduli along the corrugated direction (Gxz ) and joining direction (Gyz ) of the various core models with and without MWCNT reinforcement. An experimental and numerical investigation was performed on the various prototypes of non-uniform sandwich plates with various hybrid honeycomb core material to obtain natural frequencies and loss factors under various boundary conditions. The effect of MWCNT reinforcement in face sheet and various honeycomb core material, aspect ratio at various boundary conditions on the free vibration and transverse vibrational responses of the structure was also studied. It was found that the MWCNT reinforcement in the various honeycomb core materials increases the shear modulus considerably, thus enhancing the overall dynamic behavior of the various non-uniform honeycomb composite sandwich plates.

Perceiving emotions in the eyes: The biasing role of a fearful mouth.

The role of the eye region in interpersonal communication and emotional recognition is widely acknowledged. However, the influence of mouth expression on perceiving and recognizing genuine emotions in the eye region, especially with limited attentional resources, remains unclear. Thirty-four participants in this study completed a dual-target rapid serial visual presentation (RSVP) task while their event-related potential (ERP) data were simultaneously recorded. They were instructed to identify the type of houses and the emotional expression displayed in the eye region. The first target (T1) consisted of three upright houses, and the second target (T2) included fearful and neutral normal faces, mouth-scrambled faces, as well as composite faces (fearful eye + neutral mouth, neutral eye + fearful mouth). A robust mass univariate statistics approach was utilized to analyze the EEG data. Behaviorally, the presence of a fearful mouth facilitated recognition of the fearful eye region but hindered recognition of the neutral eye region compared to a neutral mouth. The ERP results showed that fearful expressions elicited larger N170, early posterior negativity (EPN), and P3 amplitudes relative to neutral expressions. The P1 amplitudes were increased, whereas the N170 and EPN amplitudes were reduced in response to normal and composite faces compared to mouth-scrambled faces. Collectively, these findings indicate that an unattended fearful mouth can capture covert attention and shape evaluation of eye expressions within a face, providing novel insights into the impact of visually salient mouth cues on cognitive processes involved in mind reading.

Preferred fixation position and gaze location: Two factors modulating the composite face effect.

Humans consistently land their first saccade to a face at a preferred fixation location (PFL). Humans also typically process faces as wholes, as evidenced by perceptual effects such as the composite face effect (CFE). However, not known is whether an individual's tendency to process faces as wholes varies with their gaze patterns on the face. Here, we investigated variation of the CFE with the PFL. We compared the strength of the CFE for two groups of observers who were screened to have their PFLs either higher up, closer to the eyes, or lower on the face, closer to the tip of the nose. During the task, observers maintained their gaze at either their own group's mean PFL or at the other group's mean PFL. We found that the top half of the face elicits a stronger CFE than the bottom half. Further, the strength of the CFE was modulated by the distance of the PFL from the eyes, such that individuals with a PFL closer to the eyes had a stronger CFE than those with a PFL closer to the mouth. Finally, the top-half CFE for both upper-lookers and lower-lookers was abolished when they fixated at a non-preferred location on the face. Our findings show that the CFE relies on internal face representations shaped by the long-term use of a consistent oculomotor strategy to view faces.

Frequency analysis of skew sandwich plates with polyethylene terephthalate foam-core and carbon/basalt fiber-reinforced hybrid face layers using ANFIS model and experimental validation

An adaptive neuro-fuzzy inference system (ANFIS) combines artificial neural networks (ANNs) with a Fuzzy Inference System (FIS) to predict the first three natural frequencies of basalt/carbon fiber (CF)-reinforced hybrid laminated composite face layers and Polyethylene Terephthalate (PET) foam-core skew sandwich plates. The ANFIS models are trained on large datasets, with accuracy validated through statistical methods. Elastic properties of the face layers and PET foam core are measured via uniaxial tensile and compression testing. Experimental modal analysis and numerical simulations in ABAQUS demonstrate that natural frequencies increase with skew angle and core thickness, but decrease with aspect ratios and fiber angles.

Optimizing Stretchability and Electrical Stability in Bilayer-Structured Flexible Liquid Metal Composite Electrodes.

Gallium-based liquid metals remain in a liquid state at room temperature and exhibit excellent electrical and thermal conductivities, low viscosity, and low toxicity, making them ideal for creating highly stretchable and conductive composites suitable for flexible electronic devices. Despite these benefits, conventional single-layer liquid metal composites face challenges, such as liquid metal leakage during deformation (e.g., stretching or bending) and limited elongation due to incomplete integration of the liquid metal within the elastomer matrix. To address these limitations, we introduced a bilayer structure into liquid metal composites, comprising a lower polydimethylsiloxane (PDMS) layer and an upper PDMS-liquid metal mixed layer. In the mixed layer, the liquid metal precipitates, forming a conductive network spanning both layers. This bilayer composite structure demonstrated significantly improved stretchability and elongation compared to pure PDMS or single-layer composites. Additionally, by adjusting the size and content of the liquid metal particles, we optimized the composite's mechanical and electrical properties. Under optimal conditions, spherical liquid metal particles deform into elliptical shapes under tensile stress, increasing conductive pathways and reducing electrical resistance. The combined effects of the bilayer structure and particle shape deformation enhanced the composite's stretchability and elongation, supporting its potential for flexible electronics applications.

Comparative Study of Core Material's Stiffness on Sandwich Panel with Composite Face Sheets beyond the Yield Point

The study aims to explore the performance of sandwich panels exceeding the yield point stiffness of the core material. Sandwich panels have gained growing attention among designers owing to their excellent corrosion properties, and lightweight, and speedy installation process. They have been applied in numerous industrial sectors, including aerospace, architectural, marine, and transportation. Typically, sandwich panels are composed of a single central core sandwiched by a pair of outer face sheets, where the core is normally developed using softer materials compared to the face sheets. Given that past studies have primarily focused on sandwich panels in the elastic range, this present study explored the performance of sandwich panels exceeding the yield point stiffness of the core material. The univariate search optimization method was utilized to assess the elastic modulus ratio of the core (typically foam) to the face sheet (composite material). The load was elevated in a quasi-static order until the face sheets reached their yield point. Subsequently, the panel was simulated using the finite element analysis commercial package ANSYS APDL, with simply supported boundary conditions used on all sides of the panel. The proposed model was verified by comparing the numerical and experimental data from recent literature. Based on the results, the panel's increased load-carrying capacity corresponded as the core material stiffness exceeded its yield limit. Moreover, the transmission of load to the face sheets increased as the core stiffness decreased. In summary, stiffer core materials caused the sandwich panel to behave more as isotopic face sheets. Thus, the face sheets yielded ahead of the core material.

Study on the equivalent model considering in-plane and out-of-plane mechanical properties for a curved sandwich structure with square honeycomb core

The curved honeycomb sandwich structure has larger surface area, effectively reducing the number of fasteners and parts of civil airplane fuselages, engine cowls, and ship’s hulls. Its equivalent model building can significantly shorten the simulation analysis time and improve the efficiency of optimal design and performance prediction, but the reliability and precision of the model are closely related to the calculation accuracy of equivalent elastic parameters. In the present study, an equivalent model considering the in-plane and out-of-plane mechanical properties of the core is developed for a curved square honeycomb sandwich structure. The in-plane and out-of-plane equivalent elastic parameters of the honeycomb core composed of hollow quadrangular frustum cells with symmetrical rectangular sides (i.e. square cell) are derived by analytical method, equivalent strain energy principle, and equivalent stiffness principle. And then combined with the sandwich panel theory, an equivalent model of the curved sandwich structure is established. The three-dimensional (3D) geometric models of planar honeycomb cores with hollow quadriprism cells are built by SolidWorks software, and the 3D printing is performed using Polylactic Acid (PLA) material. The three-point bending tests are conducted on the planar honeycomb sandwich structures with carbon fiber composite face sheets, and the test data are compared with the numerical results of this structure and its equivalent model. Based on ANSYS Workbench, the static analysis under bending and compression conditions, modal analysis, and harmonic response analysis are implemented on honeycomb sandwich structures with different bending degrees and their equivalent models. The results show that the load-displacement test curves of planar sandwich structures agree well with the finite element results of these structures and their equivalent models, and the equivalent models of curved sandwich structures accurately reflect the bending and vibration characteristics, fully verifying the feasibility of the presented equivalent model. This study provides important references and guidance for establishing the equivalent model of curved honeycomb sandwich structures.

Acoustic monitoring of wind turbine blades using wavelet packet analysis and 1D convolutional neural networks

Wind turbine blades (WTBs) are susceptible to faults in the harsh wind farm environments, making their safety a matter of paramount importance. Unfortunately, existing composite blade monitoring methods face various limitations in practical use. To address this issue, the study presents an intelligent fault detection method to assess the health of both the structural integrity and its skin. This has never been tried before by scholars. The study begins with the collection of acoustic signals from the blade chamber. Second, these signals are processed using wavelet packet decomposition (WPD) and Fast Fourier Transform to generate two-dimensional feature matrices. Third, apply the obtained matrices to train a one-dimensional convolutional neural network (CNN), enabling advanced feature extraction and classification, which forms the basis of the WPD-CNN model. Finally, the proposed method was experimentally verified. It has been found that the proposed WPD-CNN model achieves fault detection accuracies ranging from 87.14% to 98.57%, depending on the types of feature matrices used for training the model. These results highlight the model’s strong performance in diagnosing WTBs. Additionally, the study emphasizes the advantage of using frequency spectrum-derived features over traditional time-domain waveform features for effective WTB fault detection.

Autistic adults exhibit holistic face processing: evidence from inversion and composite face effects.

Holistic processing is commonly measured by the face inversion effect (FIE) and the composite face effect (CFE). Previous studies examining whether individuals with autism spectrum disorder (ASD) employ holistic processing using either FIE or CFE have reported inconclusive results. By adopting a customized composite face paradigm, the present study aims to simultaneously assess both the inversion and the composite effects of holistic processing in autistic and neurotypical adults. We tested 24 adults with ASD and 24 neurotypical (NT) adults matched in age, gender, and years of education. Participants viewed sequentially presented composite faces in three Presentation Modes (aligned, inverted, and misaligned) with three Stimuli Conditions (same, composite, and different) and judged whether the top half was the same. For the dependent variables, we calculated a "performance index" in the form of the accuracy/response time of each stimuli condition in each presentation mode. The FIE and CFE were computed to index the magnitude of holistic processing. Our results showed that the NT group responded more accurately in less time than the ASD group across task conditions. Notably, both the NT and the ASD groups exhibited a significant FIE with similar magnitude. Likewise, both the NT and the ASD groups showed a greater-than-zero CFE. Moreover, individuals' CFE positively correlated with FIE and negatively correlated with the AQ scores for all participants. In summary, individuals with ASD exhibit holistic processing when viewing faces, evidenced by the presence of both FIE and CFE and the positive correlations between the two effects.

Free vibration of sandwich beams with graphene nanoplatelets reinforced piezoelectric face sheets

Owing to its exceptional physical properties, graphene is considered as an excellent reinforcement for composite materials. However, the existing higher-order models encounter significant hurdles when attempting to accurately predict the natural frequencies of sandwich beams with graphene nanoplatelets reinforced composite (GNPRC) piezoelectric face sheets. If transverse shear deformations are not accurately modeled, the dynamic behavior of piezoelectric sandwich beams will be noticeably influenced by the disparities of material properties at the interfaces of adjacent layers, as well as the inherent electromechanical coupling characteristic. To overcome these challenges, an advanced electro-mechanical coupling theory will be introduced for the vibration analysis of sandwich beams featuring GNPRC piezoelectric face sheets. Compared to previous higher-order models, the proposed beam model introduces an enhanced interlaminar shear stress field incorporating electromechanical properties. This improved stress field can be involved in the equations of motion based on the Hamilton principle, significantly enhancing the precision in analyzing the free vibration behavior of piezoelectric sandwich beams. Furthermore, a simplification of finite element implementation can be achieved by removing the second-order derivatives of in-plane displacements from the transverse shear stress field. Consequently, a C0-type three-node beam element is constructed for the dynamic analysis of sandwich beams with GNPRC piezoelectric face sheets. To validate the performance of the proposed theory, the 3D elasticity solutions and results obtained from alternative theoretical frameworks are used. Numerical results demonstrate that the present model offers superior accuracy over the existing higher-order theories. Additionally, a comprehensive parametric investigation is conducted to explore the influence of key parameters on the free vibration characteristics of piezoelectric sandwich beams.

Theoretical and experimental investigation of sandwich panels with 3D printed cores with GFRP composite and aluminum face sheets under 3-point bending

Composite structures have been widely used in recent years in the aerospace industry as well as in industries that require lightweight structures due to their lightness and high strength-to-weight ratio. The purpose of this research is to introduce and compare a new type of sandwich panel to introduce structures that can be used in the aerospace industry. The cores in this research are made using Fused Deposition Modeling (FDM) with two types of PLA and ABS polymers. The face sheets used for this study are glass fiber reinforced composites and aluminum face sheets. In the first part of this study, a theoretical model for obtaining the force-displacement curve in nuclei with rectangular geometry was presented. Comparison of theoretical and experimental results showed that theoretical relationships have a good ability to predict the amount of force in experimental tests. The results obtained in this study showed that the use of composites made of epoxy glass fibers as a face sheet for sandwich panels leads to higher resistance to three-point bending. It was also found that sandwich panels made with ABS cores have higher strength compared to PLA cores, but their softness is less.

Additive manufacturing of sandwich panels with continuous fiber reinforced high modulus composite facings

AbstractAn improved approach consisting of a combination of fiber placement and fused filament fabrication is introduced for the additive manufacture (AM) of structural grade sandwich beams. Here, sandwich beams are additively manufactured using in‐situ deposition and consolidation of continuous fiber unidirectional facings made from a commingled yarn system of e‐glass fiber (~50% vol.) and amorphous PET, and a hexagonal honeycomb core structure made from PETG. Both facings and the sandwich core are manufactured on a single machine, in one sequence (skin‐core‐skin), employing the benefit of matrix compatibility to create autohesion at the interfaces. Flexural and transverse shear rigidity are determined experimentally and compared with analytical predictions and show correlation to within 3%. Flexural strength and core shear strength are also measured. Post‐mortem examinations show that core fracture and core facing debond were the dominant failure mode in flexure. Single cantilever beam tests were performed to evaluate core facing debond toughness. Subsequently, surface preheat using infrared heaters was utilized to increase autohesion between core and facing. The results show debond toughness was increased 4 times using infrared heating. This research effort presents a manufacturing approach that has the potential for the AM of stiff, well bonded, structural grade sandwich beams, in an integrated sequence, employing in‐situ consolidation to the facings, without the need for the use of intermediate adhesives for skin‐to‐core bonding.Highlights An improved additive manufacturing technique for making sandwich panels is developed. Sandwich panel facings have fiber volume fractions of approximately 50%. Surface preheat improves core‐to‐facing debond toughness by a factor of 4. Top and bottom facings are consolidated during manufacture leading to better properties. Experimental results are compared to analytical predictions and show good correlation.

Green RP-HPLC methods for assay and related substances in rivaroxaban tablets

AbstractIn this study, two different ethanol-based RP-HPLC methods for assay and quantification of rivaroxaban related substances in tablets were developed, based on green analytical chemistry (GAC) principles, using the design of experiments approach. The chromatographic separation was performed on X-Bridge C18 column (250 × 4.6 mm, 5 µm particle size), using isocratic elution with ethanol : water (35:65, % v/v) for the assay and gradient elution with ethanol/water mobile phase, for related substances, with a flow rate of 1.0 mL min−1. The gradient method was optimized for the separation of three specified impurities (impurity G, impurity H, and impurity 14) and the selectivity was further confirmed using forced degradation studies. Both methods were validated in accordance with ICH guidelines. The robustness of the methods was confirmed with the Central Composite Face Design of Experiments. Analytical Eco-scale approach and AGREE metrics confirmed that both methods are in accordance with the GAC principles. The proposed ethanol-based RP-HPLC methods were applied for assay and determination of related substances in rivaroxaban 10 mg tablets obtained from three different manufacturers available on the Macedonian market.

A hybrid of RSM, ANFIS, and machine learning algorithm approach for ultrasound-assisted extraction of bioactive compounds from Semecarpus anacardium Linn

ABSTRACT Ultrasound-assisted solvent extraction (UASE) was appslied to extract phytoconstituents from Semecarpus anacardium L. The effective extraction parameters were optimized using the Response Surface Methodology (RSM) with Central Composite Face Centered design (CCFC), and the optimization parameters were validated through Adaptive Neuro-Fuzzy Inference System (ANFIS) and Machine Learning (ML) algorithm models. The four independent parameters, i.e. ethanol concentration (X 1: 62–72% v/v), temperature (X 2: 38–48°C), ultrasonic-exposure time (X 3: 12–24 min), and particle size (X 4: 2–5 mm) were optimized. The maximum yield of phytocompounds was achieved at X 1 = 67%, X 2 = 43°C, X 3 = 18 min, and X 4 = 3.5 mm. The optimized yield of total phenolic, flavonoid, and antioxidant activity was considered when selecting process parameters. In this scenario, the optimized yields of total polyphenolic content (y1 = 619.25 mg gallic acid equivalent (GAE)/g), total flavonoid content (y2 = 151.24 mg Quercetin equivalents (QE)/g), free radical scavenging abilities (%DPPH*sc (y3 = 66.41%), and %ABTS*sc (y4 = 56.94%). The LC-MS peaks identify the presence of semecarpetin, butein, and amentoflavone, whereas GS-MS peaks represent seventeen gaseous components. Furthermore, the bioactive-rich optimized extract was nontoxic and supported the growth of macrophage- and epithelial cells in vitro. Conclusively, optimizing the UASE parameters enhanced the yield of bioactive compounds of S. anacardium L.

Design of experiments investigation into the production of all cellulose composites using regenerated cellulosic textiles

All cellulose composites (ACCs) can be produced from native and man-made cellulosic fibres; use of the latter provides an additional application for waste-derived regenerated fibers. ACCs were prepared using an ionic liquid dissolution method, utilizing a regenerated cellulose (Tencel) textile, with and without an interleaf cellulosic film. A design of experiments methodology was applied to explore process-property relationships; concentration of the ionic liquid and the processing time and temperature were investigated. It was found that the film remained in-between the textile layers, rather than penetrating the fiber assembly, in contrast to our previous work on cotton-based ACCs. This is due to the structural differences between Tencel and cotton fabric. A multi-response optimization was conducted through a central composite face centered strategy, which captured the film system more strongly. Optimized processing conditions were identified, yielding a Young’s modulus and strain-to-failure of 5.3 GPa and 3.5% respectively, validated through in-lab samples.