Composites Part C Open Access最新文献

Carbon fiber composites have emerged as a transformative technology, offering a fascinating alternative to traditional materials like aluminum and steel. Their unique combination of high strength, stiffness, and reduced density makes them an ideal choice for lightweight structural components, an attribute that aligns with the pursuit of fuel-efficient and eco-friendly aircraft designs. With the continuous race between countries and research organizations to find new materials that satisfies the above-mentioned characteristics, this article highlights the utilization of a new Ultra-Light Carbon-based Composite (ULCC) in the aeronautical sector developed within the industrial research project TERSA (Radar technologies for autonomus flying vehicles or TEcnologie Radar per Sistemi aerei a pilotaggio remoto (SAPR) Autonomi in italian). The composite material has been developed with the aim of achieving superior performance and efficiency compared to existing products on the market. To evaluate its effectiveness, first, the mechanical properties of the ULCC have been compared to T300/Epoxy and T1000/Epoxy, two of the materials commonly used in aeronautical industry and unmanned aerial vehicle (UAV). Second, finite element models were employed to verify and analyze the dynamic properties of aeronautical structural components made of ULCC. The results indicate that the new carbon-based composite exhibits remarkable strength-to-weight ratio, enhanced durability, and offering significant advantages in terms of weight reduction and overall performance. These findings validate its potential as a viable alternative in aeronautical industry.

Artificial intelligence (AI) has emerged as a pivotal tool in managing extensive datasets, enabling pattern recognition, and deriving solutions, particularly revolutionizing additive manufacturing (AM). This study intends to develop AI deep machine learning image processing techniques for real-time defects detection in additively manufactured continuous carbon fiber-reinforced polymer(cCFRP) specimens. Leveraging YOLOv8- a state-of-the-art, single-stage object detection algorithm, this study focuses on the relationship between printing parameters and defect occurrences, specifically misalignment errors. The research delineates a methodological advancement by correlating detected defects with parameter optimization, leading to significant quality improvements in cCFRP specimens. An impressive 94 % accuracy in detecting misalignments was achieved through fine-tuning the nozzle temperature adjustment, resulting in significant reductions in misalignment errors, while minimal impact is observed from print bed temperature, feed amount, and feed rate/sec on refining the proposed model for identifying optimal parameters.

In this paper, non-woven fabric and glass fiber fabric were used to prepare resin-based absorbing composite. The thermal expansion coefficient and the microwave absorbing properties of the absorbing composites with different glass fiber volume fraction were studied. The results show that the simulation results of the thermal expansion coefficient calculated by Schapery model are inconsistent with the experimental results, the metallographic results were studied to reveal that it is the added absorbent in the composite that partially replaced the resin at the interface between resin and fiber bundle causes the parameters of the material substituted in the Schapery model to be improper. A different simulation model was proposed to introduce a set of different parameters of the material to reduce the error between simulation and experiment results and the simulation results show that the error is reduced from a maximum of 53 % to a minimum of 3 %. Meanwhile the microwave absorbing properties show that the absorbing peaks of the composite materials move to low frequency with the increasing glass fiber volume fraction and the minimum reflection loss (RL) first increase and then decrease. The metallographic results show that the different distribution of absorbent in the composites within different reinforced fibers causes the movement of the absorbing peaks and the change of its minimum RL. Those research results lay a foundation for the further popularization and application of the absorbing composites.

The certification of aeronautical composite structures is based on a pragmatic approach, which is intended to be safe and essentially experimental but with a strong test/calculation dialogue called the “Test Pyramid”. However, this has proved to be extremely expensive and it appears necessary to reduce its cost either by developing Virtual testing, or by developing richer tests on an intermediate scale between coupon specimens and structural parts. It was in the aim of meeting this objective that the VERTEX program (French acronym for “Experimental modeling and Validation of compositE strucTures under complEX loading”) was launched in 2012. After positioning the VERTEX program in relation to the literature, this review will explain the methodology and present the measurement methods specifically developed for this scale. Then, three scientific themes that have been studied will be detailed (large notches, impact and wrinkling case studies). Finally, a proposal for validating the structures using envelope curves will be put forward, an assessment made, and perspectives presented.

This paper presents a comprehensive investigation into the behavior of concrete confined with hybrid Basalt and Chopped Strand Mat (B-CSM) fibers. The newly proposed B-CSM confinement technique substantially enhances the brittle compressive stress-strain behavior, leading to a noteworthy increase in peak strength (approximately 90%) and ultimate strain (approximately 461 %). The efficiency of B-CSM confinement is affected by the strength of plain concrete, with lower-strength specimens indicating a more pronounced enhancement. The performance of existing analytical models for FRP confinement in predicting ultimate strength and strain in B-CSM confined concrete is assessed, highlighting the need for tailored models. Regression-based equations are proposed for characteristic points along the stress-strain curve, enabling accurate prediction of material behavior. The predicted stress-strain curves exhibit a high level of agreement with experimental results. These findings provide valuable insights for the design and application of B-CSM confinement techniques in structural engineering, facilitating improved performance and ductility of concrete structures under compressive loading conditions.

The purpose of this study was to examine the potential impact of the testing procedure, the shape of the test sample, loading method and sample size on the K_Ic value of polymer concrete (PC) materials. The research involved experimental investigations using five different testing techniques and specimen types, namely the single edge notched beam (SENB), short bend beam (SBB), semi-circular bend (SCB), edge notch disc bend (ENDB), and center cracked Brazilian disc (CCBD). A typical PC mixture made of mineral silicious aggregate, ML506 epoxy resin, chopped E-glass, and foundry sand filler. Despite the difference in the shape and loading type of the tested samples, the K_Ic data obtained from all groups of specimens are in good agreement with together and with the SENB proposed by RILEM. Depending on the test type, the K_Ic value varied from 1.43 to 1.74 MPa.m^0.5 and the discrepancy between the data was mainly attributed to the type of loading (compression or bending) and the crack type (center crack or edge crack). The T-stress affects the fracture toughness for different testing samples and configurations. The lowest fracture toughness corresponds to the CCBD specimen (the center cracked disc loaded diametrically). The other test samples with edge cracks and loaded by a three-point bend setup showed K_Ic = 1.7 - 1.74 MPa.m^0.5. Moreover, the fracture toughness data for PC mixtures can be achieved by utilizing sub-sized samples like SBB (for smaller amounts of PC material) instead of larger beam samples (i.e., SENB).

Carbon fiber-reinforced polymers (CFRP) are widely used to strengthen reinforced concrete (RC) beams. Its major drawback is the brittle failure mode in the form of debonding of the CFRP laminate. The use of CFRP spike anchors demonstrated positive outcomes in mitigating the debonding failure in small-scale concrete prisms in previous studies. However, the real-life behavior of anchored RC beams was rarely studied . This study aims to investigate the flexural behavior of externally strengthened RC beams with CFRP laminates and anchored at end with CFRP spike anchors. The results of anchored beams was compared with unanchored specimens in terms of load-deflection response, strain in the FRP laminates, and failure modes. Results showed that anchorage of CFRP laminates with CFRP splay anchors positively affected the flexural capacity of the specimens. An average increase in the load-carrying capacity of 19 % was portrayed in the anchored specimens compared to the unanchored specimen. Anchorage of FRP laminates resulted in the mitigation of debonding failure and thus, enhanced strain utilization in laminates. A considerable improvement in strain utilization is exhibited by the specimen anchored with two anchors at each end. Moreover, increasing the anchor's dowel diameter significantly improved the load-carrying capacity but lowered the ultimate strain reached in the laminate. Results indicated that larger diameter anchors provide strengthening effect similar to increasing the number of FRP layers instead of providing anchorage to the FRP sheet. This is primarily due to the increase in the fan length and thickness as the anchor's dowel diameter increases.

This study investigates the structural behaviour of double-skin columns, introducing novel double-skin double filled tubular (DSDFT) columns, which utilise double steel tubes and concrete to enhance the load-carrying capacity and ductility beyond conventional double-skin hollow tubular (DSHT) columns, employing a combination of finite element model (FEM) and machine learning (ML) techniques. A total of 48 columns (DSHT+DSDFT) were created to examine the impact of various parameters, such as double steel tube configurations, thickness of fibre-reinforced polymer (FRP) layer, type of FRP material, and steel tube diameter, on the load-carrying capacity and ductility of the columns. The results were validated against the experimental findings to ensure their accuracy. Key findings highlight the advantages of the DSDFT configuration. Compared to the DSHT columns, the DSDFT columns exhibited remarkable 19.54 % to 101.21 % increases in the load-carrying capacity, demonstrating improved ductility and load-bearing capabilities. Thicker FRP layers enhanced the load-carrying capacity up to 15 %, however at the expense of the reduced axial strain. It was also observed that glass FRP wrapping displayed 25 % superior ultimate axial strain than aramid FRP wrapping. Four different ML models were assessed to predict the axial load-carrying capacity of the columns, with long short-term memory (LSTM) and bidirectional LSTM models emerging as superior choices indicating exceptional predictive capabilities. This interdisciplinary approach offers valuable insights into designing and optimising confined column systems. It sheds light on both double-tube and single-tube configurations, propelling advancements in structural engineering practices for new constructions and retrofitting. Further, it lays out a blueprint for maximising the performance of the confined columns under the axial compression.

This research aims to enhance the safety level and crash resiliency of targeted woven roving glass/epoxy composite material for various industry 4.0 applications. Advanced machine learning algorithms are used in this study to figure out the complicated relationship between the crashworthiness parameters of the hexagonal composite ring specimens under lateral compressive, energy absorption, and failure modes. These algorithms include random forest (RF) classification and artificial neural networks (ANN). The ultimate target is to develop a robust multi-modal machine learning method to predict the optimum geometry (i.e., hexagonal ring angle) and suitable in-plane crushing arrangements of the hexagonal ring system for targeted crashworthiness parameters. The results demonstrate that the suggested RF-ANN-based technique can predict the optimal composite design with high accuracy (precision, recall, and f1-score for test and train dataset were 1). Furthermore, the confusion matrix validates the random forest classification model's accuracy. At the same time, the mean square error value serves as the loss function for the ANN model (i.e., the loss function values were 2.84 × 10⁻⁷ and 6.40 × 10^−7, respectively, for X1 and X2 loading conditions at 45° angle). Furthermore, the developed models can predict crashworthiness parameters for any hexagonal ring angle within the range of the trained dataset, requiring no additional experimental effort.

The mass production of grapevines, an agricultural waste, has imposed burdens on farmers and the environment. This study aims to address this issue by utilizing biochar derived from grapevines (GVC) in combination with polybutylene succinate (PBS) to develop an environmentally friendly bio-composite. To enhance the compatibility between GVC and PBS, maleic anhydride grafted PBS (MAPBS) was synthesized and incorporated into the bio-composite. Moreover, surface modification of GVC was conducted using a silane coupling agent to enhance its adhesion to the PBS matrix. The effects of GVC size, MAPBS content, and surface modification on the mechanical and thermal properties of PBS were investigated. The findings indicate that GVC sieved through a 200 mesh screen exhibited a better reinforcing effect compared to GVC sieved through a 120 mesh screen. The tensile test results indicated that the incorporation of 20 wt% GVC led to a reduction in the tensile strength of PBS. However, the introduction of silane-modified GVC resulted in a substantial enhancement of tensile strength, elevating it from 33.40 MPa to 40.16 MPa. Furthermore, when the composites contained both MAPBS and a lubrication agent, the tensile strength increased even further to 41.04 MPa. The thermal analysis results of the bio-composites revealed that the addition of GVC contributed to an increase in the char yield and heat resistance of PBS. Therefore, these GVC/PBS green bio-composites not only enhance the mechanical and thermal properties of PBS but also reuse the waste grapevines, and produce the high value-added green composites with carbon-storing and biodegradability characteristics.