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This study aims to evaluate the low energy impact characteristics of 3D printed carbon fiber thermoplastic and thermoset polymer composite using the Izod impact test. The effects of infill density are examined on the Izod impact properties of 3D printed thermoset polymer and thermoplastic composite specimens. Furthermore, a thorough investigation is conducted into the effect of heat treatment using a hot-air oven on both types of 3D printed composite specimens. To characterize the impact characteristics of each specimen, the fracture surfaces caused by impact load are inspected, and the fracture mechanism is studied using scanning electron micrographs.

Izod Impact specimens of thermoset (epoxy resin) and thermoplastic carbon fiber of different infill density (70, 75, 80, 85, 90 and 100%) are fabricated using the different fiber impregnation 3D printing process. To carry out the heat treatment process, printing of composites is done for each infill design from both thermoset and thermoplastic composites and the impact characteristics of specimens are evaluated on a pendulum test-rig using the ASTM D-256 standard. Using a scanning electron microscope, each fracture zone underwent four separate scanning processes, ranging in size from 2 µm to 100 µm.

The impact resistance of the 3D printed thermoset and thermoplastic composite material is significantly influenced by the type of fiber placement and infill density in the matrix substrate. Because of the weak interfacial strength between the layers of fiber and polyamide 6, the specimen printed with continuous fiber implanted at the part exhibited reduced impact resistance. At 75% infill density, the impact specimen printed with coextruded fiber showed the highest impact resistance with a 367.02% greater magnitude than the continuous fiber specimen with the same infill density.

This work presents a novel approach to analyze the low energy impact characteristics and three-dimensional printing of carbon fiber reinforced thermoplastic and carbon fiber reinforced thermoset and thermoplastic composite material.

The purpose of this paper is to clarify the relationship between the use of different polymer matrices for the preparation of composite materials, namely, polyethylene terephthalate-glycol (PET-G) and polyamide (PA), using Composite Fiber Co-Extrusion technology with the application of two types of carbon fibers, short and continuous. The aim of the study is also to extend the knowledge of the production of composite materials with a defined structure from the point of view of their influence on the microstructure and their physical-mechanical properties.

As part of the experiment, four types of samples were prepared, namely, two types of samples with PA polymer matrix and two types with PET-G polymer matrix. All types contained short carbon fibers and always one set from each polymer matrix in addition to continuous carbon fibers. All types were prepared using the same 3D printing parameters to avoid any further influence. The samples were then tested for microstructure using microCT, mechanical properties using a tensile test and dilatation characteristics from the point of view of aerospace applications. Finally, the raw materials themselves were tested.

The paper provides insight into the influence of polymer matrix types on the physico-mechanical properties of 3D printed composites. The analysis confirmed that the physico-mechanical results varied with respect to the interface between the polymer matrix and the carbon fiber. The implications of the conclusions can be extended to the development of products in the aerospace and automotive sectors.

This study provides information for composite applications in the aerospace industry, focusing on evaluating dilatation characteristics within very low temperatures (−60 °C) when using carbon fibers (continuous carbon fibers, short carbon fibers and a combination of both) in two types of thermoplastic matrices. This perspective on materials characterisation for aerospace applications is a very important and unpublished approach within the 3D printing of composites. These characteristics are important parameters in the design of prototypes and functional samples with regard to the resulting behaviour in real conditions.

The purpose of this study is to follow up on the structural and fatigue analysis of car wheel rims with carbon fibre composites in order to ensure the vehicular safety. The wheel is an essential element of the vehicle suspension system that supports the static and dynamic loads encountered during its motion. The rim provides a firm base to hold the tire and supports the wheel, and it is also one of the load-bearing elements in the entire automobile as the car's weight and occupants' weight act upon it. The wheel rim should be strong enough to withstand the load with such a background, ensuring vehicle safety, comfort and performance. The dimensions, shape, structure and material of the rim are crucial factors for studying vehicle handling characteristics that demand automobile designers' concern.

In the present study, solid models of three different wheel rims, namely, R-1, R-2 and R-3, designed for three different cars, are modelled in SOLIDWORKS. Different carbon composite materials of polyetheretherketone (PEEK), namely, PEEK 90 HMF 40, PEEK 450 CA 30, PEEK 450 GL 40 and carbon fibre reinforced polymer-unidirectional (CFRP-UD) are used as rim materials for conducting the structural and fatigue analysis using ANSYS Workbench.

The results thus obtained in the analyses are used to identify the better carbon fibre composite material for the wheel rim such that it gives better structural properties and less fatigue. The R-3 model rim has shown better structural properties and less fatigue with PEEK 90 HMF 40 material.

The carbon composite materials used in this study have shown promissory results that can be used as an alternative for aluminium, steel and other regular materials.

The investigation aims to improve Nd: YAG laser technology for precision cutting of carbon fiber reinforcing polymers (CFRPs), specifically those containing newly created resin (NDR) from the polyethylene and polyurea group, is the goal of the study. The focus is on showing how Nd: YAG lasers may be used to precisely cut CFRP with NDR materials, emphasizing how useful they are for creating intricate and long-lasting components.

The study employs a systematic approach that includes complicated factorial designs, Taguchi L27 orthogonal array trials, Gray relational analysis (GRA) and machine learning predictions. The effects of laser cutting factors on CFRP with NDR geometry are investigated experimentally, with the goal of optimizing the cutting process for greater quality and efficiency. The approach employs data-driven decision-making with GRA, which improves cut quality and manufacturing efficiency while producing high-quality CFRP composites. Integration of machine learning models into the optimization process significantly boosts the precision and cost-effectiveness of laser cutting operations for CFRP materials.

The work uses Taguchi L27 orthogonal array trials for systematically explore the effects of specified parameters on CFRP cutting. The cutting process is then optimized using GRA, which identifies influential elements and determines the ideal parameter combination. In this paper, initially machining parameters are established at level L3P3C3A2, and the optimal machining parameters are determined to be at levels L3P2C3A3 and L3P2C1A2, based on predictions and experimental results. Furthermore, the study uses machine learning prediction models to continuously update and optimize kerf parameters, resulting in high-quality cuts at a lower cost. Overall, the study presents a holistic method to optimize CFRP cutting processes employing sophisticated techniques such as GRA and machine learning, resulting in better quality and efficiency in manufacturing operations.

The novel concept is in precisely measuring the kerf width and deviation in CFRP samples of NDR using sophisticated imaging techniques like SEM, which improves analysis and precision. The newly produced resin from the polyethylene and polyurea group with carbon fiber offers a more precise and comprehensive understanding of the material's behavior under different cutting settings, which makes it novel for kerf width and kerf deviation in their studies. To optimize laser cutting settings in real time while considering laser machining conditions, the study incorporates material insights into machine learning models.

The use of continuous fiber-reinforced filaments improves the mechanical properties obtained with the fused filament fabrication (FFF) process. Yet, there is a lack of simulation tailored tools to assist in the design for additive manufacturing of continuous fiber composites. To build such models, a precise elastic model is required. As the porosity caused by interbead voids remains an important flaw of the process, this paper aims to build an elastic model integrating this aspect.

To study the amount of porosity, which could be a failure initiator, this study proposes a two step periodic homogenization method. The first step concerns the microscopic scale with a unit cell made of fiber and matrix. The second step is at the mesoscopic scale and combines the elastic material of the first step with the interbead voids. The void content has been set as a parameter of the model. The material models predicted with the periodic homogenization were compared with experimental results.

The comparison between periodic homogenization results and tensile test results shows a fair agreement between the experimental results and that of the numerical simulation, whatever the fibers’ orientations are. Moreover, a void content reduction has been observed by increasing the crossing angle from one layer to another. An empiric law giving the porosity according to this crossing angle was created. The model and the law can be further used for design evaluation and optimization of continuous fiber-reinforced FFF.

A new elastic model considering interbead voids and its variation with the crossing angle of the fibers has been built. It can be used in simulation tools to design high performance fused filament fabricated composite parts.

In this experimental investigation, the behavior of strengthened/repaired heat-damaged one-way self-compacted concrete (SCC) slabs with opening utilizing near-surface-mounted-carbon fiber reinforced polymers (NSM-CFRP) strips was explored.

CFRP strip configurations, number of strips and inclination were all investigated in this study. For three hours, slabs were exposed to temperatures of 23°C and 500°C. Four-point load was applied to control slabs, enhanced slabs and repaired slabs.

The results indicate that exposing the slabs to high temperatures reduces their load capability. The number of strips and angle of inclination around the slab opening have a considerable impact on the performance of the strengthened and/or repaired slabs, according to the experimental results. The load capacity, toughness and ductility index of a strengthened and/or repaired slab with opening increase as the number of CFRP strips increases by 143.8–150.5%, 137.3–149.9% and 122.3–124.5%, respectively. The use of NSM strips around the opening with zero inclination showed higher load compared to the NSM strips around the opening with other angles.

It is frequently important to construct openings in the slabs for ventilation, electrical supply, and other purposes. Making openings in slabs might affect the structure’s performance since the concrete and reinforcing would be cut off. SCC is a new type of concrete mixture that can fill in all the voids in the formwork with its own weight without the help of external vibration.  As a result, it is necessary to reinforce the slab under flexure and increase the flexural strength of the SCC slab. Therefore, this work investigates the effect of using NSM-CFRP strip  on the behavior of one way SCC slabs that have been heat-damaged.

This paper aims to find the effective 3D printing process parameters based on mechanical characteristics such as tensile strength and hardness of poly lactic acid (PLA)/carbon fibre composites (CF-PLA) by implementing intelligent frameworks.

The experiment trials are conducted based on design of experiments (DoE) using Taguchi L9 orthogonal array with three factors (speed, infill % and pattern type) and three levels. The factors have been optimized by solving the regression equation which is obtained from analysis of variance (ANOVA). The contour plots are generated by response surface methodology (RSM). The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness.

The influencing parameters are found by using Box–Behnken design. The second order response surface model demonstrated the optimal combination of input parameters for higher tensile strength and hardness. The results obtained from RSM are also confirmed by implementing the machine learning classifiers, such as logistic regression, ridge classifier, random forest, K nearest neighbour and support vector classifier (SVC). The results show that the SVC can predict the optimized process parameters with an accuracy of 95.65%.

3D printing parameters which are considered in this work such as pattern types for PLA/CF-PLA composites based on intelligent frameworks has not been attempted previously.

Composite materials have revolutionized the aerospace industry by offering superior structural qualities over traditional elements. This study aims to focus on the development and testing of bamboo natural fiber-based composites enhanced with SiO₂ nanoparticles.

The investigation involved fabricating specimens with varying nanoparticle compositions (0, 10 and 20%) and conducting tensile, flexural, impact and fracture toughness tests. Results indicated significant improvements in mechanical properties with the addition of nanoparticles, particularly at a 10% composition level.

This study underscores the potential of natural fiber composites, highlighting their environmental friendliness, cost-effectiveness and improved structural properties when reinforced with nanoparticles. The findings suggest an optimal ratio for nanoparticle integration, emphasizing the critical role of precise mixing proportions in achieving superior composite performance.

The tensile strength, flexural strength, impact resistance and fracture toughness exhibited notable enhancements compared with the 0 and 20% nanoparticle compositions. The 10% composition showed the most promising outcomes, showcasing increased strength across all parameters.

The current study aims to develop a 3D-printed continuous metal fiber-reinforced recycled thermoplastic composite using an in-nozzle impregnation technique.

Recycled acrylonitrile butadiene styrene (RABS) plastic was blended with virgin ABS (VABS) plastic in a ratio of 60:40 weight proportion to develop a 3D printing filament that was used as a matrix material, while post-used continuous brass wire (CBW) was used as a reinforcement. 3D printing was done by using a self-customized print head to fabricate the flexural, compression and interlaminar shear stress (ILSS) test samples to evaluate the bending, compressive and ILSS properties of the build samples and compared with VABS and RABS-B samples. Moreover, the physical properties of the samples were also analyzed.

Upon three-point bend, compression and ILSS testing, it was found that RABS-B/CBW composite 3D printed with 0.7 mm layer width exhibited a notable improvement in maximum flexural load (L_max), flexural stress at maximum load (sf_max), flex modulus (E_f) and work of fracture (WOF), compression modulus (E_c) and ILSS properties by 30.5%, 49.6%, 88.4% 13.8, 21.6% and 30.3% respectively.

Limited research has been conducted on the in-nozzle impregnation technique for 3D printing metal fiber-reinforced recycled thermoplastic composites. Adopting this method holds the potential to create durable and high-strength sustainable composites suitable for engineering applications, thereby diminishing dependence on virgin materials.

The external confinement provided by the fiber-reinforced polymer (FRP) sheets leads to an improvement in the axial compressive strength (CS) and strain of reinforced concrete structural members. Many studies have proposed analytical models to predict the axial CS of concrete structural members, but the predictions for the axial compressive strain still need more investigation because the previous strain models are not accurate enough. Moreover, the previous strain models were proposed using small and noisy databases using simple modeling techniques. Therefore, a rigorous approach is needed to propose a more accurate strain model and compare its predictions with the previous models.

The present work has endeavored to propose strain models for FRP-confined concrete members using three different techniques: analytical modeling, artificial neural network (ANN) modeling and finite element analysis (FEA) modeling based on a large database consisting of 570 sample points.

The assessment of the previous models using some statistical parameters revealed that the estimates of the newly recommended models were more accurate than the previous models. The estimates of the new models were validated using the experimental outcomes of compressive members confined with carbon-fiber-reinforced polymer (CFRP) wraps. The nonlinear FEA of the tested samples was performed using ABAQUS, and its estimates were equated with the calculations of the analytical and ANN models. The relative investigation of the estimates solidly substantiates the accuracy and applicability of the recommended analytical, ANN and FEA models for predicting the axial strain of CFRP-confined concrete compression members.

The research introduces innovative methods for understanding FRP confinement in concrete, presenting new models to estimate axial compressive strains. Utilizing a database of 570 experimental samples, the study employs ANNs and regression analysis to develop these models. Existing models for FRP-confined concrete's axial strains are also assessed using this database. Validation involves testing 18 cylindrical specimens confined with CFRP wraps and FE simulations using a concrete-damaged plastic (CDP) model. A comprehensive comparative analysis compares experimental results with estimates from ANNs, analytical and finite element models (FEMs), offering valuable insights and predictive tools for FRP confinement in concrete.