Continuous Fiber Reinforced Polymer Composites Research Articles

A shape-performance synergistic strategy for design and additive manufacturing of continuous fiber reinforced transfemoral prosthetic socket

A shape-performance synergistic design and additive manufacturing strategy was developed for the transfemoral prosthetic sockets (TPS) to fulfill the demands of customized geometry and long-term load bearing. The 3D printing path of the continuous fiber reinforced polymer composites (CFRPC) was generated by reversing the milling path of subtractive manufacturing. The mechanical properties of the 3D printed CFRPC samples were investigated as a foundation, and the customized fiber trajectories were designed based on the analyzed stress pattern of the TPS in daily gait. The CFRPC TPS with weight of 28 % less than those made by pure resin had a safety factor larger than 3 and passed the fatigue testing of 3 million cycles. The design and additive manufacturing strategy developed in this study was also applicable for other parts with freeform surfaces.

Investigating gradient temperature control for enhanced interfacial bonding behavior in material extrusion 3D printing continuous fiber reinforced polymer composites

Interlayer delamination is a significant concern for composites manufactured by fused filament fabrication (FFF) process. To address the problem of interlayer delamination and enhance the macro-mechanical properties of FFF fiber composites, it is necessary to improve the adhesive bonding in the manufacturing process. Therefore, a novel method is provided to improve the interfacial bonding behavior of composites by controlling the nozzle temperature gradients in FFF composites along the build directions during the process. Results showed that the value of the first reheating peaks, in thermal history curves under gradient nozzle temperature settings, reached the effective temperature ranges (between melting temperatures and crystallization temperature). This enhanced the adhesive bonding between each layer. Finally, both experimental and theoretical approaches confirmed that the proposed method could enhance the interfacial behavior of FFF continuous fiber filled composites.

Novel application of dual-nozzle 3D printer for enhanced in-situ impregnation 3D printing of dry continuous fiber reinforced composites

This work reported a novel two-stage in-situ impregnation method for additively manufacturing dry fiber bundles reinforced polymer composites using a commercial dual-nozzle 3D printer. This process allowed simultaneous manufacturing of both continuous fiber prepreg filaments and continuous fiber reinforced polymer composites (CFRPCs). Initially, it was demonstrated that the two-stage in-situ impregnation method significantly enhanced the printing geometrical accuracy of specimens filled with rigid continuous carbon fiber. Subsequently, the tensile performance of CFRPCs, filled with twisted continuous ramie yarns and unwound continuous carbon fiber bundles, printed using single-stage and two-stage in-situ impregnations was compared. Significantly, the two-stage in-situ impregnation method facilitated a marked improvement in the tensile strength and modulus of CFRPCs. This improvement proved the enhanced impregnation effect of fibers, validating the effectiveness of the proposed two-stage in-situ impregnation approach for dry fiber bundles reinforced composites.

A materials informatics framework based on reduced‐order models for extracting structure–property linkages of additively manufactured continuous fiber‐reinforced polymer composites

AbstractThe innovative combination of additive manufacturing (AM) and continuous fiber‐reinforced polymer composites (CFRPCs) confers products with the dual advantages of integrated manufacturing and designability of properties, but lack an efficient and reliable method for property prediction. This study presents a materials informatics framework using reduced‐order models and machine learning (ML) to extract the structure–property (SP) linkages between microstructures and macroscopic elastic properties of AM‐CFRPCs. The initial step involves generating microstructural 2D cross‐sections and representative volume elements (RVEs) with random fiber and pore distributions based on the minimum potential method. Then, finite element (FE) calculations are performed on RVEs to obtain nine macroscopic elastic properties. Following that, the quantification and dimensionality reduction of the 2D cross‐sectional dataset are conducted separately using two‐point spatial correlations and principal component analysis (PCA). Finally, a Bayesian optimized composite kernel support vector regression (CK‐SVR) algorithm is used to effectively establish complex mapping relationships between the reduced‐order representations of the microstructures and the mechanical properties. Despite the reduced‐order dataset containing only 3–6 variables, the framework generates an interpretable model exhibiting excellent accuracy with all predicted R2 values surpassing 0.91. Therefore, this framework presents a prospective solution for expediting the design and optimization of AM‐CFRPCs.Highlights A materials informatics scheme is proposed to predict the 9 elastic properties of AM‐CFRPCs. Microstructures are quantified and dimensionally reduced by two‐point statistics and PCA. SP linkages are established between 2D cross‐sections and 3D macromechanical properties. Modified CK‐SVR exhibits higher prediction accuracy compared to conventional models.

Additive manufacturing of continuous fibre reinforced polymer composites using industrial robots: A review

In recent years, the mechanical engineering sector has undergone significant changes due to the creation and expanding application of new technologies and materials capable of radically improving the quality of manufactured products, the entire structure and production conditions. Such technologies include additive manufacturing capable of creating products from advanced materials such as continuous reinforced polymer composites. Furthermore, the integration of additive manufacturing with industrial robots offers new opportunities to create spatially reinforced composites with a directed internal structure, obtained by the orderly arrangement of continuous fibres. This review analyzes the currently available technologies for 3D printing spatially reinforced polymer composites with the addition of continuous fibers using industrial robots. The review presents the main advanced companies supplying off-the-shelf commercial systems and presents the successful experience of using these systems in the production of reinforced parts

Stress field-aware infill toolpath generation for additive manufacturing of continuous fiber reinforced polymer composites

The infill toolpath utilized in continuous fiber reinforced polymer composites via additive manufacturing (CFRPCs-AM) significantly influences the mechanical performance of printed structures. However, existing infill toolpath for CFRPCs-AM are primarily designed for 2D planar structures and do not account for 3D shell structures with complex shapes or load path dependent characteristics. To address these limitations, this paper proposes an explicit streamline tracing method to convert the stress field into adaptive infill toolpaths for CFRPCs-AM. The method leverages principal stress direction and value fields to control toolpath density and distribution while considering the minimum allowable bead width constraint. Additionally, the geodesic distance is employed to precisely represent the distance between adjacent infill toolpaths on shell structures. Experimental validation on 2D and 3D complex structures under varying conditions demonstrates the effectiveness of the proposed method, which significantly improves stiffness and strength by 200–300% compared to traditional uniform toolpaths for CFRPCs-AM.

Path Planning and Bending Behaviors of 3D Printed Continuous Carbon Fiber Reinforced Polymer Honeycomb Structures.

Continuous fiber reinforced polymer composites are widely used in load-bearing components and energy absorbers owing to their high specific strength and high specific modulus. The path planning of continuous fiber is closely related to its structural defects and mechanical properties. In this work, continuous fiber reinforced polymer honeycomb structures (CFRPHSs) with different printing paths were designed and fabricated via the fused deposition modeling (FDM) technique. The investigation of fiber dislocation at path corners was utilized to analyze the structural defects of nodes caused by printing paths. The lower stiffness nodes filled with pure polymer due to fiber dislocation result in uneven stiffness distribution. The bending performance and deformation modes of CFRPHSs with different printing paths and corresponding pure polymer honeycomb structures were investigated by three-point bending tests. The results showed that the enhancement effect of continuous fibers on the bending performance of honeycomb structures was significantly affected by the printing paths. The CFRPHSs with a staggered trapezoidal path exhibited the highest specific load capacity (68.33 ± 2.25 N/g) and flexural stiffness (627.70 ± 38.78 N/mm). In addition, the fiber distributions and structural defects caused by the printing paths determine the stiffness distribution of the loading region, thereby affecting the stress distribution and failure modes of CFRPHSs.

3D/4D Printed Functional Continuous Fiber-reinforced Polymer Composites: Progress and Perspectives

In recent years, innovations in 3D/4D printing techniques for continuous fiber-reinforced polymer composites (CFRPCs) have opened new perspectives for the integrated design and manufacture of composites with customized functions. This paper reviews the current state of 3D/4D printed functional composites, including the materials, shape memory/changing effects, self-monitoring/healing behaviors, and challenges surrounding additive-manufactured functional composites. Specifically, continuous fibers and matrices that provide functional roles are classified and discussed in detail. 4D printed shape memory and changing CFRPCs can retain their original shapes from a designed shape upon exposure to different external stimuli, including heat, electricity, humidity, and multi-stimuli activation. Furthermore, self-monitoring of structural health is achieved through the piezoresistive features of reinforced fibers in 3D printed CFRPCs. Finally, this review concludes with an outlook on the future research opportunities for 3D/4D printed functional CFRPCs.

Discrete in-situ consolidation of additively manufactured continuous fiber-reinforced polymer composites

Discrete in-situ consolidation of additively manufactured continuous fiber-reinforced polymer composites

Ply level strain measurement in composites using digital image correlation

Application of digital image correlation technique for full field strain characterization has gained widespread interest. However, characterization of the strain and damage at very small length scales using this technique is still difficult and development in this regard would add a great value in understanding the material behavior, particularly for in-homogeneous materials such as composites. This article describes the work done primarily on continuous fiber reinforced polymer composites and ceramic matrix composites, towards applying the digital image correlation (DIC) technique at very small length scale, of the order of 100–200 μm. A novel technique was developed to generate a fine and randomly distributed speckle pattern. Actual examples of the application of digital image correlation at small length scales for strain and damage characterization are briefly described such as ply level strain measurement near ply drop in a fiber reinforced composite, local carbon-glass fiber joint strain measurement in hybrid composites, and short beam shear strength validation using ply level strain measurement in carbon glass fiber joints in hybrid composites. Limitations of digital image correlation technique for small scale strain measurement are also discussed.

Progressive concurrent topological optimization with variable fiber orientation and content for 3D printed continuous fiber reinforced polymer composites

In this paper, a multidisciplinary framework to design and produce a topological structure with inhomogeneous distributions of fiber orientation and content for 3D printed continuous fiber reinforced polymer composites (CFRPCs) was proposed to maximize load and fiber utilization efficiency. The combination of optimization design and 3D printing for composites with curvilinear fiber was achieved that the fiber orientation and content were progressively designed in the two-stage procedure. The fiber orientation and topological structure were concurrently optimized firstly. Then, the fiber content was designed through finite element analysis (FEA) and realized by changing continuous fiber path with a hierarchical structure design method. Based on this, the topological beam has been additively manufactured and experimentally investigated, showing 38.55% and 25.40% in stiffness and peak load enhancement compared with the original CFRPCs design, while 264.39% and 165.84% increments compared with pure polymer design. The proposed approach has widened design freedom of CFRPCs 3D printing, possessing potential application to solve engineering problem in aerospace, automotive and other fields.

Deep learning-assisted real-time defect detection and closed-loop adjustment for additive manufacturing of continuous fiber-reinforced polymer composites

• A closed-loop control approach is successfully developed for online detection of defects and adjustment of process parameters by deep learning, which is never achievable in any conventional methods of composite fabrication. • Deep learning is implemented to detect the CFRP defects for AM in real-time with high accuracy and low latency. The proposed method is able to identify different types of CFRP defects (i.e., misalignment and abrasion). • The combined deep learning with geometric analysis of the level of misalignment is applied to quantify the severity of individual defects. Real-time defect detection and closed-loop adjustment of additive manufacturing (AM) are essential to ensure the quality of as-fabricated products, especially for carbon fiber reinforced polymer (CFRP) composites via AM. Machine learning is typically limited to the application of online monitoring of AM systems due to a lack of accurate and accessible databases. In this work, a system is developed for real-time identification of defective regions, and closed-loop adjustment of process parameters for robot-based CFRP AM is validated. The main novelty is the development of a deep learning model for defect detection, classification, and evaluation in real-time with high accuracy. The proposed method is able to identify two types of CFRP defects (i.e., misalignment and abrasion). The combined deep learning with geometric analysis of the level of misalignment is applied to quantify the severity of individual defects. A deep learning approach is successfully developed for the online detection of defects, and the defects are effectively controlled by closed-loop adjustment of process parameters, which is never achievable in any conventional methods of composite fabrication.

Performance Study of 3D Printed Continuous Fiber-Reinforced Polymer Composites Using Taguchi Method

Performance Study of 3D Printed Continuous Fiber-Reinforced Polymer Composites Using Taguchi Method

Fully Predictive Micro-mechanical Modelling for Shear Viscosities of Continuous Fiber Reinforced Polymer Composites

Fully Predictive Micro-mechanical Modelling for Shear Viscosities of Continuous Fiber Reinforced Polymer Composites

Direct-write 3D printing of UV-curable composites with continuous carbon fiber

Continuous fiber-reinforced polymer composites, with their superior combinations of high stiffness, high strength, lightweight, and corrosion resistance, have been leading contenders in various applications ranging from aerospace to ground transportation. The integration with 3D printing allows the composite products to be customized quickly and fabricated inexpensively to meet unique specifications. However, the material-process-property relationships of 3D printed thermoset composites with continuous fiber are insufficiently explored in the field. In this work, a cost-effective 3D printing method is developed based on the direct ink writing method to print UV-curable composites reinforced with continuous carbon fiber. During the printing, the fiber tow is impregnated with an acrylate ink inside the printer head and then extruded onto the printer bed, where it is cured upon UV irradiation. The influences of different material and processing variables, such as the resin viscosity, nozzle size, printing speed, and substrate temperature, on the quality and mechanical properties of composites, are investigated. The porosity, stiffness, and strength of printed symmetric laminate are also examined. Finally, the 3D printing of composite structures and the conformal coating of curved surfaces along a defined printing path are demonstrated. The findings reported in this work could contribute to the rapid design and manufacturing of composite components with unprecedented mechanical properties and emerging functionalities for the automotive, defense, and aerospace industries.

Investigation of flexural properties in 3D printed continuous fiber-reinforced polymer composites

Fiber-reinforced polymers and their advancements have been remarkably addressed in the past few decades. In recent years, additive manufacturing (AM) technology has added a new dimension to the polymer composite industry. This article is intended to explore the AM technique, in particular, fused deposition modeling (FDM), to highlight the effect of various printing parameters on flexural properties. Flexural specimens were prepared as per ASTM D790 standard with a Nylon-based matrix (Onyx) and continuous fibers, such as glass fiber, Kevlar fiber, and carbon fiber using Markforged Mark Two 3D printer. After analysis of the results, it was observed that the flexural properties are highly influenced by the print parameters. Fiber type and isotropic fiber angles were found as the most influencing parameters for flexural strength and modulus responses. After reinforcement, the flexural strength was increased by 76.4% for Kevlar fiber, 129.8% for glass fiber, and 230.1% for carbon fiber. This work will provide the researchers in the 3D printing domain to gain an insight into the characteristics of fiber-reinforced polymeric composites.

Effect of repeated thermal cycle on compressive properties of cyanate ester/quartz-based composites

The continuous fibre reinforced polymer composites are used in electronic circuits and radar radomes of defence aircraft due to lower transmission loss in the broadband microwave frequency region. Quartz fibre-reinforced cyanate ester composites are one of the best-suited materials for the radome structure of an aircraft offering exceptional electromagnetic transparency in varying operational temperatures. Moreover, this material possesses high-temperature capability with excellent mechanical and dielectric properties. The radome structure usually undergoes multiple thermal cycles during fabrication. In this work, to determine the compressive strength, a quartz/cyanate ester laminate was prepared with repeated thermal infusion followed by final curing (CE/Q_M) using resin film infusion (RFI) technique. The volume fraction of fibre, resin and void fraction were evaluated using the resin-burn off method. Finally, Ultrasonic C-scanning, a non-destructive quality evaluation and raster scanning using a multi-axis squirter system was carried out to detect and characterize the defects induced in the laminate. These data were compared to a sample produced with regular infusion followed by a curing cycle (CE/Q_S). The compressive strength of CE/Q_M (372.48 MPa) is measured to be 12% more than that of CE/Q_S (332.084 MPa). This is attributed to increased crosslink density in CE resin and strong interfacial adhesion between the resin and the fibre in the latter due to multiple thermal infusion cycles.

Hierarchical Composite Materials

Conventional continuous fiber reinforced polymer composites (FRP) have extensively been used as structural elements in a myriad of sectors, such as civil, transport, energy and marine, among other, due to their superior mechanical properties, low weight and ease of processing. However, the relatively weak compression and interlaminar properties of these composites limit their application field. Interest is, therefore, growing in the development of hierarchical or multiscale composites, in which, a nanoscale filler reinforcement is utilized to alleviate the existing limitations associated with the matrix dominated properties. The majority of the work has focused on carbon nanotubes, CNTs due to their extraordinary intrinsic properties. In addition to their outstanding mechanical properties, CNTs possess excellent electrical and mechanical properties, making them attractive materials as reinforcement for polymer matrices. CNTs provide both intralaminar and interlaminar reinforcement, thus improving delamination resistance and through thickness properties, without compromising in-plane performance. In recent years, there has been an increasing interest in the use of graphene as reinforcement of polymer nanocomposites. Compared to CNTs, graphene has advantages such as lower cost, higher surface area and a greater ease of processing. Graphene is more easily dispersed in the resin and causes a lower viscosity increase, which can facilitate the impregnation of the fibers. In this work, the fabrication and characterization of hierarchical composites are analyzed through the inclusion of graphene to conventional continuous carbon fiber reinforced epoxy composites by vacuum-assisted resin transfer molding.

A PFA methodology to investigate UD composites in fatigue comprising a KTF-based model for matrix damage and stochastic fiber failure prediction

A physics-based, multiscale, progressive fatigue damage analysis methodology for continuous fiber-reinforced polymer composites was further developed and applied to the problem of fatigue damage prediction in open hole tension (OHT) coupons comprised of multidirectional laminates undergoing tension–tension loading. Based off lamina level calibration data, cumulative damage analysis of matrix and fiber constituents under cyclic loading condition was implemented with the objectives to: (i) predict the onset and the progression of subcritical matrix failures as well as the ultimate failure due to fiber fracture, and (ii) observe the effects of damage mode interactions on the progressive failure process. A kinetic theory of fracture (KTF) based durability prediction methodology was utilized to keep track of the matrix damage transpiring in the intra- and inter-laminar layers of the laminate. Tensile strengths were randomly assigned to the fibers using two-parameter Weibull statistics, while the maximum stress theory in tandem with the linear damage accumulation rule was applied to capture the tensile rupture of the reinforcements. Progressive fatigue damage prediction of OHT coupons consisting of the unidirectional composite IM7/977-3 and layup schedules of [0/45/90/−45]2S and [60/0/−60]3S were carried out and benchmarked against available experimental data. It was observed that the KTF-based fatigue model captured the damage state at individual layers, overall loss of mechanical performance, and stochastic nature of the ultimate failure well. The effects of interactions among the damage modes, namely matrix cracking, delamination, and fiber rupture were also investigated and it was recognized that such interactions are a critical feature of the progressive failure process in composite structures.

Additive manufacturing of continuous fiber reinforced polymer composites: Design opportunities and novel applications

The development of additive manufacturing (AM) has revolutionized the fabrication process of continuous fiber reinforced polymer (CFRP) composites with its outstanding ability to create products that are complex in material, structure, and function. However, existing studies mostly focus on the manufacturing process without paying sufficient attention to exploring new design opportunities enabled by CFRP-AM. This paper outlines the design concept for additive manufacturing of continuous fiber reinforced polymer composites, aiming to improve existing products' performance and foster product innovations for future needs. More specifically, existing CFRP-AM techniques are reviewed from the perspective of functional requirements. Design opportunities within the material, process, and structure domains are identified. Also, novel applications enabled by design for CFRP-AM on shape morphing, sensing, and energy storage are presented. The review concludes with a brief discussion and an outlook on future work.