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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 review paper is to provide a review of the most recent advances in the field of manufacturing composite sandwich panels along with their advantages and limitations. The other purpose of this paper is to familiarize the researchers with the available developments in manufacturing sandwich structures.

The most recent research articles in the field of manufacturing various composite sandwich structures were reviewed. The review process started by categorizing the available sandwich manufacturing techniques into nine main categories according to the method of production and the equipment used. The review is followed by outlining some automatic production concepts toward composite sandwich automated manufacturing. A brief summary of the sandwich manufacturing techniques is given at the end of this article, with recommendations for future work.

It has been found that several composite sandwich manufacturing techniques were proposed in the literature. The diversity of the manufacturing techniques arises from the variety of the materials as well as the configurations of the final product. Additive manufacturing techniques represent the most recent trend in composite sandwich manufacturing.

This work is valuable for all researchers in the field of composite sandwich structures to keep up with the most recent advancements in this field. Furthermore, this review paper can be considered as a guideline for researchers who are intended to perform further research on composite sandwich structures.

Wind energy has developed rapidly becoming a promising source of renewable energy. Although wind energy is described as clean energy, the problem of blade disposal has emerged from decommissioned wind turbines in the renewable energy sector, these blades manufactured from composite materials are almost impossible to recycle.

This study proposed a methodological workflow for an educational approach toward accelerating the transition to a circular economy (CE) through a case study reusing wind turbine blade waste. The participants were undergraduate students. In the quantitative case study approach of students’ coursework, innovative architectural reuse was the basis of the methodology for creatively reusing blade waste. Students reused the blades as building elements.

The workflow could be beneficial to the renewable energy sector and the architecture, engineering and construction industry. The results show that the impact of creative reuse is positive as it reduces the energy consumed by conventional recycling processes, reduces carbon dioxide-equivalents and preserves the structural properties of the blades.

The research reported in this study is exploratory and findings may not be generalizable due to the location and limited number of participants in the design process. Also, the empirical data collected were limited to the views and opinions of the students and instructor.

The novel workflow provided evidence at the end of the course that participating students became more interested in CE and were able to think more independently about CE. Creative reuse promotes circularity, reducing virgin material extraction and carbon emissions.

The purpose of this study is to assess the incorporation of fiber reinforced polymer (FRP) bars in concrete as a reinforcement enhances the corrosion resistance in a concrete structure. However, FRP bars are not practically used due to a lack of standard codes. Various codes, including ACI-440-17 and CSA S806-12, have been established to provide guidelines for the incorporation of FRP bars in concrete as reinforcement. The application of these codes may result in over-reinforcement. Therefore, this research presents the use of a machine learning approach to predict the accurate flexural strength of the FRP beams with the use of 408 experimental results.

In this research, the input parameters are the width of the beam, effective depth of the beam, concrete compressive strength, FRP bar elastic modulus and FRP bar tensile strength. Three machine learning algorithms, namely, gene expression programming, multi-expression programming and artificial neural networks, are developed. The accuracy of the developed models was judged by R², root means squared and mean absolute error. Finally, the study conducts prismatic analysis by considering different parameters. including depth and percentage of bottom reinforcement.

The artificial neural networks model result is the most accurate prediction (99%), with the lowest root mean squared error (2.66) and lowest mean absolute error (1.38). In addition, the result of SHapley Additive exPlanation analysis depicts that the effective depth and percentage of bottom reinforcement are the most influential parameters of FRP bars reinforced concrete beam. Therefore, the findings recommend that special attention should be given to the effective depth and percentage of bottom reinforcement.

Previous studies revealed that the flexural strength of concrete beams reinforced with FRP bars is significantly influenced by factors such as beam width, effective depth, concrete compressive strength, FRP bars’ elastic modulus and FRP bar tensile strength. Therefore, a substantial database comprising 408 experimental results considered for these parameters was compiled, and a simple and reliable model was proposed. The model developed in this research was compared with traditional codes, and it can be noted that the model developed in this study is much more accurate than the traditional codes.

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.

This study aims to introduce a predictive homogenization model incorporating electrical percolation considerations to forecast the electrical characteristics of unidirectional carbon-epoxy laminate composites.

This study presents a method for calculating the electrical conductivity tensor for various ply arrangement patterns to elucidate phenomena occurring around the interfaces between plies. These interface models are then integrated into a three-dimensional (3D) magneto-thermal model using the finite element method. A comparative study is conducted between different approaches, emphasizing the advantages of the new model through experimental measurements.

This research facilitates the innovative integration of electrical percolation considerations, resulting in substantial improvement in the prediction of electrical properties of composites. The validity of this improvement is established through comprehensive validation against existing approaches and experimentation.

The study primarily focuses on unidirectional carbon-epoxy laminate composites. Further research is needed to extend the model's applicability to other composite materials and configurations.

The proposed model offers a significant improvement in predicting the electrical properties of composite materials by incorporating electrical percolation considerations at inter-ply interfaces, which have not been addressed in previous studies. This research provides valuable information to improve the accuracy of predictions of the electrical properties of composites and offers a methodology for accounting for these properties in 3D magneto-thermal simulations.

In order to improve the efficiency and reliability of simulation analysis for composite riveting structures in engineering products, a comparative study was conducted on different forms of riveting simulation methods.

Five different rivent simulation models were established using the finite element method, including rigid element CE, flexible element Rbe3 and beam element, and their results were future compared and analyzed.

Under the given technical parameters, the simulation method of Rbe3 (with holes) + beam can meet the analysis requirements of complex engineering products in terms of the rationality of rivet load distribution, calculation error and relatively efficient modeling.

This study proposes a simulation method for the riveting structure of carbon fiber composite materials for engineering applications. This method can satisfy the simulation analysis requirements of transportation vehicles in terms of modeling time, computational efficiency and accuracy. The research can provide technical support for the riveting process and mechanical analysis between carbon fiber composite components in transportation products.

The aim of this study is to investigate the printing parameters of fused deposition modeling (FDM), a material extrusion-based method, and to examine the mechanical and thermal properties of their polylactic acid (PLA) components reinforced with copper, bronze, and carbon fiber micro particles.

Tensile test samples were created by extruding composite filament materials using FDM-based 3D printer. Taguchi method was used to design experiments where layer thickness, infill density, and nozzle temperature were the printing variables. Analysis of variance (ANOVA) was applied to determine the effect of these variables on tensile strength.

The results of this study showed that the reinforcement of metal particles in PLA material reduces strength and increases elongation. The highest tensile strength was obtained when the layer thickness, infill density, and nozzle temperature were set to 100 µm, 60%, and 230 °C, respectively. As a result of thermal analysis, cooper-PLA showed the highest thermal resistance among metal-based PLA samples.

It is very important to examine the mechanical and thermal quality of parts fabricated in FDM with metal-PLA composites. In the literature, the mechanical properties of metal-reinforced composite PLA parts have been examined using different factors and levels. However, the fabrication of parts using the FDM method with four different metal-added PLA materials has not been examined before. Another unique aspect of the study is that both mechanical and thermal properties of composite materials will be examined.

This paper aims to reduce waste management and generate wealth by investigating the novelty of combining chicken feather fiber and bamboo particles to produce hybrid biocomposites. This is part of responsible production and sustainability techniques for sustainable development goals. This study aims to broaden animal and plant fiber utilization in the sustainable production of epoxy resins for engineering applications.

This research used two reinforcing materials [chicken feather fiber (CFF) and bamboo particles (BP)] to reinforce epoxy resin. The BPs were kept constant at 6 Wt.%, while the CFF was varied within 3–15 Wt.% in the composites to make CFF-BP polymer-reinforced composite (CFF-BP PRC). The mechanical experiment showed a 21% reduction in densities, making the CFF-BP PRC an excellent choice for lightweight applications.

It was discovered that fabricated composites with 10 mm CFF length had improved properties compared with the 15 mm CFF length and pristine samples, which confirmed that short fibers are better at enhancing randomly dispersed fibers in the epoxy matrix. However, the ballistic properties of both samples matched. There is a 40% increase in tensile strength and a 54% increase in flexural strength of the CFF-BP PRC compared to the pristine sample.

According to the literature review, to the best of the authors’ knowledge, this is a novel study of chicken fiber and bamboo particles in reinforcing epoxy composite.

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.