Search Results (447)

Predicting the transverse tensile properties of unidirectional CFRP plies is often based on micromechanical representative volume elements (RVEs) with circular fiber cross-sections, whereas microscopic observations show pronounced ellipticity and size variability in actual fibers. A two-dimensional plane-strain micromechanical framework with elliptical fiber cross-sections is developed as a virtual testing tool to quantify how fiber volume fraction, cross-sectional aspect ratio and statistical fluctuations in the semi-minor axis influence the transverse tensile response. Random RVEs are generated by a hard-core random sequential adsorption procedure under periodic boundary conditions and a minimum edge-to-edge gap constraint, and the fiber arrangements are validated against complete spatial randomness using nearest-neighbor statistics, Ripley’s K function and the radial distribution function. The matrix is described by a damage–plasticity model and fiber–matrix interfaces are represented by cohesive elements, so that high equivalent-stress bands in matrix ligaments and the associated crack paths can be resolved explicitly. Parametric analyses show that increasing fiber volume fraction raises the transverse elastic modulus and peak stress by thinning matrix ligaments and promoting longer, more continuous high-stress bands, while the cross-sectional aspect ratio redistributes high stress among ligaments and adjusts the balance between peak strength and the degree of failure localization. The observed size variability is represented by modeling the semi-minor axis as a normal random variable; a larger variance mainly leads to a reduction in transverse peak stress through stronger stress localization near very thin ligaments, whereas the elastic slope and the strain at peak stress remain almost unchanged. The proposed framework thus provides a statistically validated and computationally efficient micromechanical basis for microstructure-sensitive assessment of the transverse behavior of UD-CFRP plies with non-circular fiber cross-sections. Full article

While carbon fiber strength is typically characterized through single-fiber tensile tests, these isolated measurements do not account for the local mechanical constraints present within a composite architecture. This study employs a synergistic computational micromechanics approach combining finite element analysis (FEA) and analytical modeling to investigate how the surrounding matrix influences the Stress Intensity Factor (SIF) and the apparent ultimate strength of embedded fibers. By calculating the J-integral, we demonstrate that the matrix provides a significant shielding effect, constraining crack opening displacements and substantially reducing the SIF. This mechanism results in a marked increase in in situ fiber tensile strength relative to dry fibers. Incorporating this matrix-adjusted Weibull distribution into a longitudinal failure model significantly improves the prediction of fiber-break density accumulation, showing closer correlation with experimental benchmarks than traditional models. Full article

This study evaluated the ballistic performance and failure mechanisms of epoxy-based hybrid laminates reinforced with abaca/UHMWPE and pineapple leaf fiber (PALF)/UHMWPE fabrics fabricated by using vacuum-assisted hand lay-up. Ballistic tests utilized 9 mm full metal jacket (FMJ) rounds (~426 m/s impact velocity) under NIJ Standard Level IIIA conditions (44 mm maximum allowable BFS). This experimental test was complemented by finite element analysis (FEA) incorporating an energy-based bilinear fracture criterion to simulate matrix cracking and fiber pull-out. The results showed that abaca/UHMWPE composites exhibited lower backface signature (BFS) and depth of penetration (DOP) values (~23 mm vs. ~42 mm BFS; ~7 mm vs. ~9 mm DOP) than PALF/UHMWPE counterparts, reflecting superior interfacial adhesion and more ductile failure modes. Accelerated weathering produced matrix microcracking and delamination in both systems, reducing overall ballistic resistance. Scanning electron microscopy confirmed improved fiber–matrix bonding in abaca composites and interfacial voids in PALF laminates. The FEA results reproduced major failure modes, such as delamination, fiber–matrix debonding, and petaling, and identified stress concentration zones that agreed with experimental observations, though the extent of delamination was slightly underpredicted. Overall, the study demonstrated that abaca/UHMWPE hybridcomposites offer enhanced ballistic performance and durability compared with PALF/UHMWPE laminates, supporting their potential as sustainable alternatives for lightweight protective applications. Full article

This study presents a comparative investigation of the dynamic compression behaviors of steel fiber-reinforced cementitious composites (SFRCC) and steel fiber-reinforced concrete (SFRC) under elevated temperatures up to 800 °C, utilizing a split Hopkinson pressure bar (SHPB). The experimental results demonstrate that SFRCC exhibits enhanced overall performance at high temperatures, maintaining a progressive failure mode and approximately 40% residual strength even at 800 °C, while SFRC experiences rapid deterioration beyond 600 °C. In the low-to-medium temperature range of 200–400 °C, SFRCC shows significantly higher dynamic peak stress and toughness compared to SFRC. However, in the high-temperature range of 600–800 °C, the superior thermal stability of the aggregate–matrix system in SFRC results in better performance in these metrics. The findings provide insights into the damage evolution mechanisms of fiber-reinforced cement-based materials under coupled thermal and dynamic loads, offering a critical theoretical foundation for material selection in engineering structures exposed to extreme thermal environments. Full article

The study proposes a methodology that combines digital image correlation (DIC) with cluster analysis (CA) to investigate the damage evolution and localization behavior of basalt fiber foam concrete (BFFC) under tensile loading. This method can simultaneously conduct quantitative analysis of both the process of damage accumulation and the process of damage localization. Quasi-static tensile tests were performed on specimens with different matrix densities and basalt fiber content. The full-field and full-process deformation images of the specimens were recorded by a high-resolution CCD. Cluster analysis was performed on the precise deformation data obtained from the DIC method, and damage extent factors and damage localization coefficients were defined. Statistical analysis indicates that the incorporation of basalt fibers not only effectively delays the progression of damage in foam concrete materials but also significantly enhances their initial damage threshold load and inhibits the phenomenon of damage localization in foam concrete. Compared to specimens without basalt fibers, those incorporating basalt fibers exhibited increases in the damage localization coefficients at tensile failure of 0.4, 0.33 and 0.18, respectively, under three different matrix density conditions. Therefore, the proposed DIC-CA method, in conjunction with the defined damage extent factor and damage localization coefficient, can effectively and quantitatively capture the two key dimensions of damage (accumulation extent and spatial distribution characteristics) in fiber-reinforced foam concrete under tensile loading. This provides an efficient, intuitive, quantitative analysis method for characterizing the initiation, development and localization processes of damage in similar materials. Full article

This study investigates the influence of braiding angles on the mechanical behavior and damage mechanisms of three-dimensional (3D) braided composites under uniaxial compressive and tensile loading. By integrating uniaxial compression and tension tests with finite element (FE) analysis, the relationships between mesoscale damage initiation, propagation, and the macroscopic mechanical properties were revealed. Results demonstrate that the 3D4d-20° model exhibits higher stiffness and compressive strength but lower compressive failure strain compared to the 3D4d-40° model, attributed to differences in fiber spatial arrangement and matrix cracking propagation. Conversely, the 3D4d-40° model shows enhanced tensile performance but greater matrix-dominated damage under tension. Moreover, as the braiding angle increases, the ratio of tensile strength to compressive strength in 3D braided composites decreases accordingly. Comparative analysis of damage evolution pathways reveals that smaller braiding angles (20°) initiate damage earlier under compression, while larger angles (40°) promote transverse fiber bundle failure and matrix degradation. This research not only elucidates the underlying microscale damage mechanisms of 3D braided composites under compression loading but also highlights the differences in damage patterns between compressive and tensile loading, providing theoretical foundations for structural design and performance optimization of such composite materials. Future work will focus on incorporating interfacial effects and manufacturing-induced defects to refine the model further. Full article

Additive manufacturing offers several advantages, such as rapid prototyping, cost-efficient production, and flexibility in constructing components. This work presents the design and fabrication of a strain sensor capable of generating electrical signals under applied mechanical loads, enabling potential failure prediction. The sensor was manufactured using the Fused Deposition Modeling 3D-printing process, combining acrylonitrile-styrene-acrylate as structural and protective layers with a conductive polylactic acid matrix containing carbon nanotubes as the sensing element. To assess its performance, the sensor was embedded within a Glass Fiber-Reinforced Polyester composite and subjected to bending tests. The results demonstrate a reliable sensing response, characterized by a measurable increase in electrical resistance under bending load. Moreover, the change in resistance increased with applied bending force, demonstrating the sensor’s feasibility for structural health monitoring applications. Full article

Additive Manufacturing (AM) using Fused Layer Modelling (FLM) often results in polymer components with limited and highly anisotropic mechanical properties, exhibiting structural weaknesses in the layer direction (Z-direction) due to low interlaminar adhesion. The main objective of this work was to investigate and quantify these mechanical limitations and to develop strategies for their mitigation. Specifically, this study aimed to (1) characterize the anisotropic behavior of unreinforced Polycarbonate (PC) components, (2) evaluate the effect of continuous, unidirectional (UD) carbon fiber tape reinforcement on mechanical performance, and (3) validate experimental findings through Finite Element Method (FEM) simulations to support predictive modeling of reinforced FLM structures. Methods involved experimental tensile and 3-point bending tests on specimens printed in all three spatial directions (X, Y, Z), validated against FEM simulations in ANSYS Composite PrepPost (ACP) using an orthotropic material model and the Hashin failure criterion. Results showed unreinforced samples had a pronounced anisotropy, with tensile strength reduced by over 70% in the Z direction. UD tape integration nearly eliminated this orthotropic behavior and led to strength gains of over 400% in tensile and flexural strength in the Z-direction. The FEM simulations showed very good agreement regarding initial stiffness and failure load. Targeted UD tape reinforcement effectively compensates for the weaknesses of FLM structures, although the quality of the tape–matrix bond and process reproducibility remain decisive factors for the reliability of the composite system, underscoring the necessity for targeted process optimization. Full article

Traditional concrete materials have limitations in terms of load-bearing capacity and ductile failure. In contrast, Engineered Cementitious Composites (ECCs), with their superior strain-hardening behavior and multiple cracking characteristics, have attracted widespread attention in the field of high-performance materials. In this study, ECC specimens incorporating different types of fibers (polyethylene (PE) fibers, polyvinyl alcohol (PVA) fibers) at varying contents were tested to systematically analyze their influence on mechanical properties. Compressive, flexural, and uniaxial tensile strength tests were conducted to evaluate the mechanical performance of ECCs. In addition, scanning electron microscopy (SEM) was employed to examine the fracture surfaces of the fibers, providing deeper insights into the interfacial behavior and fracture morphology of the different fiber-reinforced systems. Fracture surface analysis reveals that the interfacial bonding characteristics between different fibers and the matrix significantly influence fracture behavior. Moreover, as the tensile performance of ECCs is influenced by the interaction of multiple factors, traditional constitutive models exhibit limitations in accurately predicting its complex nonlinear behavior. To address this limitation, an Artificial Neural Network (ANN) approach was adopted to develop a predictive model based on bilinear stress–strain relationships. The model was constructed using ten key input parameters, including matrix composition and fiber properties, and was able to accurately predict the first cracking strain, first cracking stress, ultimate strain, and ultimate stress of ECCs. Sensitivity analysis revealed that fiber tensile strength and fiber content were the most significant factors influencing the tensile behavior. The predicted tensile curves showed strong consistency with the experimental results, thereby confirming the reliability and applicability of the proposed ANN-based model. Full article

Composite materials have been extensively used across numerous industries due to their exceptional specific strength and corrosive resistance. However, ensuring their mechanical performance and structural integrity remains a critical challenge. This study provides an in-depth investigation into the damage mechanisms occurring in composite rebars manufactured via a modified pultrusion process, with a special emphasis on carbon, glass, and hybrid continuous fiber-reinforced polymers with epoxy resin matrix subjected to static tensile loading. To reveal the damage development, the acoustic emission (AE) technique was employed. Given the inherent complexity of composite microstructures, multiple failure modes can occur simultaneously, often masked by background noise and attenuation effects. Therefore, the core objective of this research is to evaluate and quantify the influence of acoustic attenuation on damage assessment in composite materials. This study introduces an optimization approach to minimize discrepancies between signals captured by different sensors, thereby enhancing the reliability of AE data interpretation. Results reveal that attenuation is strongly dependent on signal travel distance, frequency spectrum, and sensor type. Importantly, a data correction methodology is proposed to mitigate these effects, improving the accuracy of damage detection. Among the analyzed AE parameters, the initial frequency emerged as the most reliable feature for identifying the origin of acoustic events within hybrid composite structures. This finding represents a significant step toward more precise, attenuation-compensated acoustic emission monitoring, offering improved insight into failure mechanisms and contributing to the development of smarter diagnostic tools for composite materials. Full article

The present study investigated the tensile behavior, failure mechanisms and deformation characteristics of glass fiber-reinforced polymer (GFRP) composites with symmetric [0°/90°/90°/0°] and asymmetric [0°/90°/0°/90°] stacking sequences across a temperature range of 30–150 °C. Tensile testing revealed superior mechanical performance in the symmetric lay-up, with higher tensile strength and failure strain sustained across elevated temperatures. Failure mode analysis revealed a transition from ductile failure to brittle failure with increasing temperature, which was more pronounced in the asymmetric lay-up, along with increased delamination and reduced fiber pull-out. Failure surface examination supported these findings, revealing better interfacial bonding and matrix integrity in the symmetric lay-up. Deformation analysis further confirmed a more homogeneous distribution of strain and longer failure time in symmetric laminates. Across all the metrics, including toughness, energy absorption, and strain uniformity, the symmetric configuration outperformed the asymmetric counterpart, underscoring the critical role of balanced stacking in enhancing the thermal durability. The observed temperature-induced degradation and its impact on mechanical and failure behavior emphasize the need for temperature-sensitive design strategies in GFRP-based structures. Full article

Industry 4.0, in its search for improvements to processes and efficient products, has increasingly invested in the use and development of high-performance materials for its production lines. This is exemplified by the introduction of CFRP in the aeronautical industry, since this composite material has reduced the weight of aircraft and improved their performance. For the construction of large structures, drilling processes are also necessary to fix parts. However, this machining process can cause failures in the structure as a whole. These structural failures occur due to fragmentation, tearing, or detachment of the matrix fiber, significantly reducing the quality and reliability of the final equipment. In this scenario, it is important to preventively detect these intrinsic production failures that lead to the condemnation of the final parts. One indirect detection method is acoustic emission. This work presents a feasibility study focused on the application of data-driven methods for delamination detection and tool wear monitoring in composite machining. A setup for a helical interpolation end-milling drilling process was performed under varying machining conditions, from mild to severe, on CFRP composite plates. Acoustic emission (AE) signals were acquired at each machining pass. The methodology involved selecting an optimal frequency band to obtain information about the wear of the drilling tool through RMS and power spectral density (PSD) analysis, followed by using correlation indices to characterize tool wear progression. The results demonstrate the potential of spectral and statistical techniques to support real-time monitoring and decision-making in advanced composite manufacturing. Full article

Ordinary concrete exhibits inherent brittleness, which restricts its deformation capacity and durability under extreme loading conditions. Engineered cementitious composites (ECC) have been developed to address these limitations; however, conventional ECC often suffers from relatively low compressive strength, limiting its use in demanding structural applications. To overcome this drawback, high-strength ECC (HS-ECC) was prepared by incorporating high-volume mineral admixtures and three types of synthetic fibers-polypropylene (PP), polyethylene (PE), and polyvinyl alcohol (PVA). This study aimed to investigate the influence of fiber type and dosage on the flexural behavior of HS-ECC and to propose a toughness evaluation framework better suited to its strain-hardening characteristics. A comprehensive experimental program, including compressive and four-point bending tests, was conducted to evaluate failure modes, flexural performance, and post-cracking behavior. Results showed that PE fibers significantly enhanced flexural strength and toughness, PP fibers provided superior deformability at higher dosages, while PVA fibers tended to fracture due to strong matrix bonding, limiting their effectiveness in high-strength matrices. Based on the observed load–deflection responses, a physically meaningful flexural toughness evaluation method was developed, which reliably captured elastic, hardening, and softening stages of HS-ECC. The findings not only clarify the role of different fiber types in HS-ECC but also offer a new evaluation approach that can guide fiber selection and mix design for structural applications. Full article

Fiber tow-gaps and overlaps formed during the Automated Fiber Placement (AFP) process pose a significant challenge by introducing non-uniform composite morphologies, often characterized by resin-rich regions and fiber waviness. These defects occur as deposited fibers sink into the gap regions during consolidation, with gap geometry determined during path planning. Such morphological inconsistencies can compromise structural reliability by initiating premature failure, particularly through localized out-of-plane waviness and resin accumulation. This study investigates the integration of high melting temperature thermoplastic veils, specifically polyetherimide (PEI), into fiber tow-gaps as a method to prevent ply sinking and reduce fiber waviness on both internal and external surfaces of the laminate. The PEI veils also serve to reinforce resin-rich regions by forming an interpenetrated network of high fracture toughness material within the brittle epoxy matrix. Tensile tests conducted on cross-ply laminates containing staggered gaps demonstrated that the inclusion of PEI veils modified the failure mode. The results suggest that the selective placement of thermoplastic veils within tow-gaps during AFP offers a viable strategy to mitigate manufacturing-induced non-uniform morphologies. Full article

Carbon Fiber-Reinforced Polymer (CFRP) composites, widely used across industries, exhibit various damage mechanisms depending on the loading conditions applied. This study employs a structural health monitoring (SHM) approach to investigate the three primary failure modes, fiber breakage, matrix cracking, and delamination, in thermoset quasi-isotropic CFRPs subjected to quasi-static tensile loading until failure. Acoustic emission (AE) signals acquired from an experiment were leveraged to analyze and classify these real-time signals into the failure modes using machine learning (ML) techniques. Due to the extensive number of AE signals recorded during testing, manually classifying these failure mechanisms through waveform inspection was impractical. ML, alongside ensemble learning, algorithms were implemented to streamline the classification, making it more efficient, accurate, and reliable. Conventional AE parameters from the data acquisition system and feature extraction techniques applied to the recorded waveforms were implemented exclusively as classification features to investigate their reliability and accuracy in classifying failure modes in CFRPs. The classification models exhibited up to 99% accuracy, as depicted by evaluation metrics. Further studies, using cross-correlation techniques, ascertained the presence of fiber break events occurring in the bundles as the thermoset CFRP composite approached failure. These findings highlight the significance of integrating machine learning into SHM for the early detection of real-time damage and effective monitoring of residual life in composite materials. Full article