Composites Part C Open Access最新文献

This article explores how the mechanical properties of composite polymers reinforced with jute fibers are influenced by manufacturing conditions, specifically pressure and temperature. To investigate this, a total of 45 distinct samples were created, and fabricated under nine different pressure and temperature conditions. The results demonstrate a notable linear increase in mechanical properties with incremental changes in pressure, while the impact of temperature variations remains less clearly defined. Based on these findings, a corrective factor was developed for the homogenization formula or rule of mixture that is commonly used to predict the mechanical behavior of composite polymers but does not typically consider manufacturing conditions. The newly introduced corrective factor aims to improve the accuracy of predictions and represents a significant advancement in modeling jute fiber-reinforced composite polymers. This development opens the door for more precise predictions and a better understanding of the intricate relationship between manufacturing conditions and resulting material properties.

This study presents an automatic mesh generation algorithm designed to address computational challenges in simulating small-scale defects within large composite structures. The algorithm seamlessly transitions from a coarse mesh, corresponding to the global structure, to a highly refined mesh in targeted local regions of interest. The transition element number and shape can be adjusted by the specified parameters. Tailored to complement this method for non-homogeneous composite models, which include multiple materials such as cohesive layers representing interlayer properties, a volume fraction calculator is integrated to automatically assign the mixture material property in each transition element. Entire processes are fully automated using a MATLAB script, eliminating the need to open the FEA software interface. The validation studies of the reconstructed two-dimensional models, assembled with the wrinkle-defect model, demonstrate their feasibility. The performance of the model is examined in terms of strain and displacement at the connecting boundaries, load–displacement curve, and interlayer failure prediction. The mesh transition model achieves agreeable results compared to a fully fine mesh model, and a 92% reduction in computational time in stress analysis, showing the efficiency of the mesh transition for local–global modeling of composite structures.

Metal rubber, which has the advantages of low density, strong environmental adaptability, and excellent design flexibility, is widely applied in manufacturing industries such as the aerospace, shipping, and automotive industries. Based on the research object of flexible porous metal rubber (FPMR) structures made of high-temperature elastic alloys, this study established a constitutive model for the quasi-static mechanical properties of FPMR structure under ultra-wide temperature range conditions. Firstly, the forming mechanism and the influencing factors of the static stiffness properties of the FPMR micro-structure were analyzed. Then, the theoretical model of the FPMR micro-element spring was established by applying the cylindrical spiral compression spring stiffness theory, and the theoretical model was corrected based on the large deformation theory and numerical analysis methods. A comparative analysis was carried out through the corrected theoretical model and the test results of different test samples. And the results show that the corrected theoretical model can comprehensively reflect the nonlinear quasi-static stiffness characteristics of the FPMR structure in an ultra-wide temperature range. More importantly, by comparison with the prediction models proposed by other scholars, it is proved that the model proposed in this paper has higher prediction accuracy and the goodness of fit R² is closer to 1, which provides a theoretical basis for the application of metal rubber in flexible support structures under ultra-high temperature environments.

Integrating ceramic matrix systems into coreless filament winding (CFW) enables the creation of sustainable, heat- and fire-resistant fiber composite lightweight structures. This study introduces a chemically bonded ceramic matrix system based on metakaolin, tailored for space applications utilizing lunar resources. The system employs acidic activation for processing with basalt/mineral fibers and alkaline activation for carbon fibers composites. Initially, the constituents of the matrix system are outlined, alongside potential synthesis pathways from lunar resources. Various formulations, incorporating different additives, are proposed. Through coupon compression testing, the most performative formulations for each activation type are selected for further investigation. The addition of zirconium silicate resulted in a higher compressive strength without significantly affecting the compressive modulus. The study then proceeds to experimentally characterize the matrix system’s viscosity. Subsequently, the processability of the proposed matrix system with CFW is demonstrated through the fabrication of generic medium-size lattice samples. Finally, these samples undergo destructive structural testing in compression. While emphasizing material development aspects, the investigation concludes that the feasibility of the proposed concept is validated through the successful fabrication and testing of generic CFW samples, affirming its potential use in space-related structural applications.

This paper presents a study on the development and validation of a nonlinear finite element model for laminated composite shells, that considers a first-order shear deformation theory (FSDT) and an explicit through-thickness integration. The model integrates a meso-scale damage analysis that considers progressive matrix and fiber failures. The model is compared with envelopes of experimental curves extracted from 3-point bending test coupons and shows accurate predictions.

In recent years, an increasing interest in innovative solutions design of aircraft structural components has been raised through both research and industrial fields, aimed at optimising weight and enhancing the ability to withstand both static and dynamic loads. This study compares the structural response to a bird strike phenomenon of a vertical tail of a UAV in standard metallic configuration with the one obtained from an innovative solution, equal in volume but with an internally designed architecture for an additive approach and manufactured by employing a carbon fibre reinforced filament engineered for metal replacement applications (carbon fibre, CF/polyamide, PA). The additive solution proposes the use of a 10 % infill and a lattice structure that completely replaces the traditional aircraft structure concept. This approach leads to a significant weight reduction, approximately 45 % compared to the traditional metallic configuration. The investigation was conducted through explicit numerical simulations considering different impact angles. The numerical model of the bird strike has been assessed by numerical-experimental comparison, simulating the impact of a bird with a flat plate. For this study, the Coupled Eulerian-Lagrangian (CEL) approach has been adopted to perform the simulation. The results were compared in terms of stress distribution, failure analysis, displacements, and energy-time and force-time diagrams. The work demonstrated that using innovative manufacturing processes, such as additive manufacturing, can significantly improve the bird strike resistance of aerospace structures. This improvement is achieved though the production of lighter, structurally collaborative geometries, by reducing the load transferred to the rest of the UAV by about 47 % and decreasing the displacement on the impact area by 53 %.

3D lattice structures comprise a connected network of segments that allow positioning of the base material where needed while maintaining an open-cell characteristic. These structures represent an ideal lightweight core material for high-performance sandwich panels. This work presents, for the first time, the performance of lattice-based cores fabricated via indirect additive manufacturing using pultruded Carbon Fiber Reinforced Polymer (CFRP) rods. The CFRP sandwich panels were tested under out-of-plane compression, and their compressive properties and failure modes were predicted via analytical and FE analyses, later contrasted with mechanical testing. Finally, the study compares favorably with similar core materials found in the literature.

Individual Layer Fabrication (ILF) is a novel additive manufacturing process that was developed to create objects with high wood content and high mechanical strength. Here, thin and individually contoured wood composite panels are created via Binder Jetting and subsequent mechanical pressing. Like in Sheet Lamination, these panels are then laminated onto each other to create a three-dimensional object. With wood contents (more than 85 mass percent) and mechanical properties (more than 30 MPa flexural strength) on par with other engineered wood products like particle boards and plywood, the produced objects are well suited for the construction and furniture industry. To gain a deeper understanding of the process, the influence of processing parameters on the geometric and mechanical properties of the finished objects were investigated. As process parameters the amounts of adhesive and the pressing forces for both panel production and lamination were selected. It was discovered that the interaction between the amount of adhesive and the pressure used to produce the panels is highly relevant for the geometric properties. The three core mechanisms that are responsible for the mechanical properties of produced parts were identified and can be ranked in the following order: 1) the amount of adhesive in the panels binding the particles, 2) the density of the panels, 3) the amount of adhesive for laminating the panels.

There is a global emphasis on recycling and reuse of plastic waste. Despite constituting over one-third of the world's annual plastic production, only 10 % of polyethylene is recycled. This study explores the use of fused deposition modeling (FDM) to enable the recycling of industrial waste of low-density polyethylene (LDPE) blended with expanded polystyrene (EPS). Two LDPE/EPS ratios (50/50 and 70/30) were investigated, and two types of styrene-ethylene-butylene-styrene (SEBS) rubber were incorporated as compatibilizers. The mechanical, rheological, thermal, and morphological properties of these blends were analyzed to assess their printability. Results indicate that the use of SEBS enhances the mechanical properties, thermal stability, and morphological uniformity of the blends. Particularly, malleated SEBS exhibited superior compatibilizing ability, fostering strong interactions at the LDPE/EPS interface. The best blend, based on printability assessments, was the 50/50 LDPE/EPS ratio with a 5 wt% malleated SEBS. Consequently, this blend was extruded into feedstock filaments, and it was successfully printed via FDM. The proposed blends are anticipated to perform effectively in various applications and serve as a foundation for future development of wear-resistant materials. The outcomes of this study present a novel approach for upcycling LDPE waste while promoting sustainable FDM practices.

We propose and demonstrate a computational framework to obtain data-driven surrogate constitutive models capturing the mechanical response of anisotropic brittle solids displaying progressive anisotropic damage. We train the constitutive models on data obtained from the analysis of a volume element of a material of interest; the data is generated by a constitutive model for braided composites, displaying a complex anisotropic damage evolution progressively transitioning from transversely isotropic to orthotropic. Training involves imposing six-dimensional random strain histories on the physical model and recording the histories of stress, strain and homogenised stiffness matrix of the material, obtained by a set of linear perturbation analyses. Supervised machine learning and dimensionality reduction are applied to the data and a structure for a surrogate model is proposed. The surrogate predicts the evolution of the stiffness of the solid consequent to an arbitrary imposed six-dimensional strain increment, thereby calculating the corresponding increment in stress. The model displays high accuracy and is able to reproduce the homogenised material's response via simple neural networks.