Short Fiber Reinforced Polymer Composites Research Articles

Influences of cryo-thermal cycling on the tensile properties of short carbon fiber/polyetherimide composites

The reliability of short fiber reinforced polymer composites is crucial for their successful applications in aerospace engineering with cryo-thermal cycling (CTC). This study investigates the influences of CTC on the tensile properties of short carbon fiber (SCF)/polyetherimide (PEI) composites at room temperature (25 °C) and elevated temperature (170 °C). First, SCF surfaces were coated by carbon nanotube-polydomaine (CNT-PDA) hybrid sizing, and then SCF/PEI composites were prepared via the facile injection molding technique. Impressively and unexpectedly, the tensile strength at 25 °C and 170 °C of SCF/PEI composites is all enhanced by the CTC exposure, attributing to the enhanced mechanical properties of PEI matrix and fiber/PEI interface bonding. In addition, CNT-PDA sizing and CTC exposure have a combined effect on improving the tensile properties of SCF/PEI composites at 25 °C and 170 °C. Overall, the SCF/PEI composites exhibit excellent mechanical properties under extreme service conditions, and are thus promising to serve in aerospace components.

Multiscale-based multiaxial fatigue model of short fiber reinforced polymer composites under high-cycle proportional loading

Due to the inhomogeneity and anisotropy of short fiber-reinforced polymers (SFRP), even uniaxial loading can induce multiaxial stress states inside them, which significantly increases the difficulty of grasping their fatigue behavior. To efficiently predict the fatigue life of SFRP under proportional multiaxial stress, a multiaxial high-cycle fatigue model is proposed, relying upon the multiscale modeling strategy capable of integrating the influence of fiber microstructure. Taking the fiber-matrix interface stress at the critical region as the internal driving factor of fatigue fracture is the core assumption verified by microscopic observations. Off-axial fatigue tests with different orientations and stress ratios are performed to validate the fatigue model. Results show that the prediction accuracy has reached an acceptable level, and the quadratic polynomial surface can well represent the relationship between fatigue life and multiaxial stresses. This work provides an efficient tool for multiaxial fatigue life prediction and expounds the failure behavior of SFRP from multiscale perspectives.

A machine learning-based prediction of biaxial failure envelope of a short fiber-reinforced polymer composite

A machine learning-based prediction of biaxial failure envelope of a short fiber-reinforced polymer composite

Fatigue Damage Assessment and Lifetime Prediction of Short Fiber Reinforced Polymer Composites—A Review

This paper reviews the findings in the area of fatigue damage assessment and lifetime prediction of short fiber reinforced polymer composites (SFRPs) under cyclic loading. It is shown that the direct methods of microstructure/damage inspection are the most sensitive and informative, while micro-computed tomography (μ-CT) is more laborious and possesses limitations in sample dimensions. Although the sensitivity of the indirect methods can vary, the most common one is based on stiffness reduction. It is shown that developing models of fatigue processes is impossible without assessing the degree of damage. The latter can be determined by stiffness reduction, the development of creep, or energy dissipation. Since fatigue mechanisms can differ, the most complete information can be obtained by combining these methods. The prediction results for fatigue life models based on plastic strain development showed the greatest agreement with the experimental results in comparison with other prediction models. In addition, some tasks are highlighted as the priority directions for the development of SFRPs and non-destructive testing (NDT) methods for their monitoring under fatigue.

Development of Screw-Based 3D Printing Machine and Process Experiments for Short Fiber Reinforced Polymer Composites

3D printing is one of the flexible additive manufacturing (AM) processes that can be used to fabricate parts from various types of materials such as polymers, metal, and ceramic. 3D printing process is one of the famous techniques for printing the product from the filament causing material degradation. Granule-based 3D printing or screw-based material extrusion 3D printing is an alternative process that can create the parts from plastic or composite granule raw materials. However, there are limited use and study in the designation of granule-based 3D printing and process parameters including material temperature, heat bed temperature, nozzle size, and printing speed. These process parameters play a significant role in the properties of 3D printing parts. Some parameters cannot be adjusted in the commercial 3D printing process. Thus, the purposes of this study are to develop a screw-based material extrusion 3D printing machine that can freely adjust the process parameters and to investigate the effect of 3D printing parameters on the appearance and mechanical properties of printed parts. Pellets of neat acrylonitrile butadiene styrene (ABS) and short glass fiber/ABS composites are used in the experiments. Six process parameters were studied, including % fiberglass, printing temperature, printing speed, nozzle size, % Infill, and heat bed temperature. Each parameter has 3 levels, which were designed by the Taguchi L18 method. The results were evaluated by the main effect plot method and showed that the printing speed, nozzle size, and %fiberglass are the top 3 parameters that affect tensile strength. The nozzle size, %infill, and %fiberglass are the top 3 parameters that affect Young’s modulus. The granule-based 3D printing machine was completely developed; however, the extruded plastic line from the nozzle was difficult to control resulting in poor product quality. Thus, the feedback control for controlling the screw-extruder speed and temperature will be developed in future work.

A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites.

This study aims to develop a high-generalizability machine learning framework for predicting the homogenized mechanical properties of short fiber-reinforced polymer composites. The ensemble machine learning model (EML) employs a stacking algorithm using three base models of Extra Trees (ET), eXtreme Gradient Boosting machine (XGBoost), and Light Gradient Boosting machine (LGBM). A micromechanical model of a two-step homogenization algorithm is adopted and verified as an effective approach to composite modeling with randomly distributed fibers, which is integrated with finite element simulations for providing a high-quality ground-truth dataset. The model performance is thoroughly assessed for its accuracy, efficiency, interpretability, and generalizability. The results suggest that: (1) the EML model outperforms the base members on prediction accuracy, achieving R2 values of 0.988 and 0.952 on the train and test datasets, respectively; (2) the SHapley Additive exPlanations (SHAP) analysis identifies the Young's modulus of matrix, fiber, and fiber content as the top three factors influencing the homogenized properties, whereas the anisotropy is predominantly determined by the fiber orientations; (3) the EML model showcases good generalization capability on experimental data, and it has been shown to be more effective than high-fidelity computational models by significantly lowering computational costs while maintaining high accuracy.

Numerical simulation and experimental validation of ratchetting deformation of short fiber-reinforced polymer composites

This paper proposes a micromechanical model based on the finite element method (FEM) to analyze the ratchetting deformation of short glass fiber-reinforced polymer composites (SFRPCs). To reasonably consider the complex mechanical characteristics of the polymeric matrix under asymmetrical stress-controlled cyclic loading, a damage-coupled nonlinear viscoelastic-viscoplastic (VE-VP) cyclic constitutive model was constructed. The cyclic constitutive model of the matrix was introduced into the FEM model utilizing a user-defined material, and the corresponding experimental validation of the matrix was conducted. Subsequently, the cyclic constitutive model was introduced into the analysis of the micro-FEM model of the composites. The random orientation was considered to generate fibers in the FEM. Based on the analysis of the microscale representative volume element (RVE) model, the effects of different fiber content and cyclic peak stress on the ratchetting of SFRPCs were studied in detail. After validation, the effects of the fiber length and fiber stiffness on the ratchetting deformation of SFRPCs are discussed based on the developed micro-FEM model. The results show that the increase in the fiber length and fiber stiffness reduced the ratchetting deformation of SFRPCs within a specific content range. The model can assess the ratchetting deformation and damage evolution within the composites to provide more accurate predictions of the ratchetting deformation of SFRPCs and guide the expanded application of SFRPCs in structures.

Analysis of sample size for friction test of short fiber reinforced polymer composites

AbstractAccording to the recommendations of literature and standards, the minimum sample size for the friction test of thermoplastics is usually 3–5. While this size is valid for the friction test of neat polymer resins with homogeneous structures, its applicability to short fiber reinforced polymer composites with inhomogeneous structures remains unclear because the uneven distribution of discontinuous fibers or fillers in the matrix leads to the performance of the composites being dispersed. In this work, as a typical example, 100 friction samples made of short carbon fiber reinforced polyetherimide (SCF/PEI) composite with a typical content of 10 vol% SCFs are prepared, and their friction coefficients are measured. The maximum likelihood estimation and goodness of fit methods are employed to assess the fitting degree between the overall friction coefficient results and five mathematical distributions. The results show that the overall friction coefficient data distribution is in good agreement with the Lognormal, Generalized extreme value (GEV), and Burr distributions, respectively. Based on the fitting result, the minimum sample size is analyzed by Monte Carlo Simulation. It shows that the minimum sample size strongly depends on the property dispersion parameter (COV: coefficient of variation). Based on this parameter, the minimum sample size is given. In a practical test, the performance dispersion degree of a composite cannot be known in advance, hence a sample size determination method is proposed to yield a reasonable performance description parameter by applying the results of this work, and this method is validated by the overall test data.Highlights Proposed a sample size determination method for practical friction tests. The minimum sample size for the friction test is yielded statistically. The minimum sample size strongly depends on a property dispersion parameter.

Fast and synergetic fatigue life prediction of short fiber reinforced polymer composites from monotonic and cyclic loading behavior

Grasping the fatigue behavior of short fiber reinforced polymers (SFRP) is difficult due to varying microstructures throughout the material cause variations in material properties. To efficiently predict the stress versus number of cycles to failure (S–N) curve of SFRP featuring any microstructure, a synergetic framework stemming from quasi-static damage and cyclic modulus degradation is proposed. Firstly, well-designed tests are conducted, and an intrinsic relationship between the modulus degradation and the fatigue life is captured. Secondly, the microstructure-independent relationships among the first cycle damage (FCD), the stable relative modulus degradation rate (SRMDR), and the fatigue life are deduced and demonstrated from multi-scale perspectives. Finally, FCD-based and SRMDR-based approaches are adopted to generate S–N data in the low-life and high-life regions, respectively. Results indicate that the synergistic method retains the efficiency of the individual method and further reduces the error by more than 80%. Moreover, discoveries herein can equip engineers with powerful S–N prediction tools in practice and reveal the fatigue mechanism of SFRP.

Microscopic void distribution of 3D printed polymer composites with different printing direction

The microscopic void distribution of 3D printed short fiber reinforced polymer composites with different printing direction was studied in this paper. The internal voids including the independent voids and connected voids were quantitatively counted by high-resolution X-ray micro-computed tomography (μCT) method. After the CT scan, the specimens were subjected to compression tests. Experimental results showed that the void volume of the specimens with the 0° printing direction was much higher than that with 45°/−45° and 0°/90° printing directions, and the connected voids were dominant. By comparison, the out-of-plane compressive resistance of 0° printed specimen was lower than that of the other two printing directions because of higher proportion of internal voids.

Modeling and characterizations of mechanical behaviors of short carbon fiber and short glass fiber reinforced polyetherimide composites

As the major manufacturing methods for short fiber reinforced polymer (SFRP) composites, extrusion compounding and injection molding techniques usually lead to complex fiber length and orientation distributions in final composites for mechanical computation analysis. It is still a challenging task about how to accurately predict the comprehensive mechanical behaviors (tensile properties, damage evolutions and failure modes) of SFRPs from complicated microstructures. This work builds a new finite element model (FEM) based on the classical random sequential adsorption algorithm with genetic algorithm (GA) for high-efficiency geometrical construction for short carbon fiber and short glass fiber reinforced polyetherimide (SCF/PEI and SGF/PEI) composites. Fiber length and orientation measurements, single fiber filament and pure PEI tensile tests are carried out to support model establishment. It is found that the longer initial length would accompany with the longer final average length and the higher fiber volume fraction would bring about shorter average final length. Short carbon fibers with larger aspect ratios tend to be more preferentially aligned along mold flow direction than short glass fibers. The predicted tensile properties, damage evolutions and failure modes are in good agreement with experimental results. Finally, the effects of fiber volume fractions, orientations and lengths on the strength and modulus of SCF/PEI and SGF/PEI composites are quantitatively explored, respectively. Our proposed model can provide an efficient evaluation of the mesoscopic structure-property relationship of SFRP composites.

Bayesian inference of fiber orientation and polymer properties in short fiber-reinforced polymer composites

We present a Bayesian methodology to infer the elastic modulus of the constituent polymer and the fiber orientation state in a short-fiber reinforced polymer composite (SFRP). The properties are inversely determined using only a few experimental tests. Developing composite manufacturing digital twins for SFRP composite processes, including injection molding and extrusion deposition additive manufacturing (EDAM) requires extensive experimental material characterization. In particular, characterizing the composite mechanical properties is time consuming and therefore, micromechanics models are used to fully identify the elasticity tensor. Hence, the objective of this paper is to infer the fiber orientation and the effective polymer modulus and therefore, identify the elasticity tensor of the composite with minimal experimental tests. To that end, we develop a hierarchical Bayesian model coupled with a micromechanics model to infer the fiber orientation and the polymer elastic modulus simultaneously which we then use to estimate the composite elasticity tensor. We motivate and demonstrate the methodology for the EDAM process but the development is such that it is applicable to other SFRP composites processed via other methods. Our results demonstrate that the approach provides a reliable framework for the inference, with as few as three tensile tests, while accounting for epistemic and aleatory uncertainty. Posterior predictive checks show that the model is able to recreate the experimental data well. The ability of the Bayesian approach to calibrate the material properties and its associated uncertainties, make it a promising tool for enabling a probabilistic predictive framework for composites manufacturing digital twins.

Rapid fiber alignment quantification in direct write printing of short fiber reinforced composites

Short fiber reinforced polymer composites fabricated by direct ink writing (DW) have design flexibility, low cost, and tailored property advantages over traditional composite materials. Controlling fiber alignment in these composite systems can be used to design unique materials with direction-dependent properties, especially when coupled to topology optimization for light weighting and high strength. . This study focuses on controlling fiber alignment in short carbon fiber composites with two printing parameters common in DW systems: nozzle diameter and velocity ratio. To do so, a novel rapid fiber alignment analysis (RFAA) was developed to quickly collect large amounts of fiber alignment data. Compared to the conventional fiber alignment quantification technique, X-Ray CT, RFAA is a rapid, accurate, and low-cost alternative. It was determined that fiber alignment can be enhanced by printing with smaller nozzle diameters and higher velocity ratios. Longitudinal bending stiffness and bending strength improved as the fiber alignment increased. Composites printed with low fiber alignment exhibited isotropic mechanical properties. However, aligning the fibers in the composite resulted in direction-dependent properties with significantly enhanced modulus and strength in the alignment direction.

Fatigue failure mechanism analysis and life prediction of short fiber reinforced polymer composites under tension-tension loading

Short fiber reinforced polymer (SFRP) composites have won extensive applications in the automotive industry. For precisely expounding the fatigue failure mechanism and predicting the fatigue life of SFRP composites, a novel micromechanical fatigue model of SFRP dominated by the fiber–matrix interface is proposed. A newly designed three-layer reinforcement structure is introduced to construct the representative volume element (RVE) model. The numerical analysis is performed under the envelope load damage framework and verified via fatigue tests and in situ X-ray imaging. Results show that the progressive fatigue damage of each constituent can be quantitatively analyzed. Moreover, the S-N data of SFRP featuring specific orientation tensor and stress ratio is well predicted, which can be applied to reduce the fatigue test costs and improve the durability of SFRP components.

Process parameters effect on environmental sustainability of composites FFF technology

ABSTRACT The present study aims at investigating the effect of process parameters on environmental sustainability of 3D printing of short fiber-reinforced polymer composites. A preliminary study allowed defining the printing parameters, in terms of printing speed, extrusion temperature and layer thickness, which guarantee low energy consumption. Then, the environmental behavior of two 3D printable composite materials, glass fiber-reinforced (GlassPA) and carbon fiber-reinforced (CarbonPA) polyamide, was investigated using the Life Cycle Assessment methodology, to provide a comprehensive overview of the considered materials and to support the sustainable development of industrial additive manufacturing processes. The functional unit was chosen taking into account the mechanical properties of the two short fiber-reinforced composites. To this purpose, tensile and flexural tests were performed on specimens produced by Fused Filament Fabrication process to evaluate the mechanical properties of printed materials. Scanning electron microscopy was used to observe the different filament morphology affecting the materials performances. Experimental tests showed that CarbonPA exhibits mechanical performances higher than those of GlassPA. Due to the weight reduction that can be accomplished by means of carbon fibers, CarbonPA results the most environmentally friendly alternative in tensile loads applications. On the contrary, for flexural loads applications, GlassPA exhibits lower environmental footprint.

A machine learning approach to determine the elastic properties of printed fiber-reinforced polymers

This work focuses on the simultaneous determination of the elastic constants and the fiber orientation state for a short fiber-reinforced polymer composite by performing a minimum of experimental tests. We introduce a methodology that enables the inverse determination of fiber orientation state and the in-situ polymer properties by performing tensile tests at the composite coupon level. We demonstrate the approach for the extrusion deposition additive manufacturing (EDAM) process to illustrate one application of the methodology, but the development is such that it can be applied to short fiber-reinforced polymer (SFRP) systems processed via other methods. Currently, developing composites additive manufacturing digital twins require extensive material characterization. In particular, the mechanical characterization of the orthotropic elastic properties of a composite involves extensive sample preparation and testing, therefore the elasticity tensor is generally populated using a micromechanics model. This, however, requires measuring the fiber orientation state in addition to knowing the constituent material properties. Experimentally measuring the fiber orientation state can be tedious and time consuming. Further, optical methods are limited to resolving the orientation of cylindrical fibers or cluster of non-cylindrical fibers, and computed tomography (CT) methods scan regions of volume that are much smaller than a full printed bead. Therefore, we propose a methodology, accelerated by machine learning, to identify the anisotropic mechanical properties and fiber orientation state at the same time. Early results show that inference of the fiber orientation and composite properties is possible with as few as three tensile tests. Our results show that a combination of the choice of the micromechanics model and reliable set of experiments can yield the nine elastic constants, as well as, the fiber orientation state.

Study of material homogeneity in the long fiber thermoset injection molding process by image texture analysis

To quantify the homogeneity of fiber dispersion in short fiber-reinforced polymer composites, a method for image texture analysis of 3-dimensional X-ray micro computed tomography (µCT) images is presented in this work. The adaption of the method to the specific requirements of the composite material is accomplished using a statistical region merging approach. Subsequently, the method is applied for evaluating the homogeneity of specimens from an intermediate step of the long fiber thermoset injection molding process as well as molded parts. This new injection molding process enables the manufacturing of parts with a flexible combination of short and long glass fibers. By using a newly developed screw element based on the Maddock mixing element design, the material homogeneity of parts molded in the long fiber injection molding process is improved.

Extrusion width critically affects fibre orientation in short fibre reinforced material extrusion additive manufacturing

This study develops new understanding of fibre orientation distribution in material extrusion additive manufacturing of short fibre reinforced polymer composites. Short carbon fibre reinforced polylactic acid was 3D printed at several nozzle temperatures (T n ), printing speeds (V), and extrusion widths set in the GCode (W set ), to identify the effects of these parameters on fibre orientation. Microscopic characterisation of the top surface of 3D printed filaments allowed detailed analysis of fibre orientation including dependencies on extruded filament width and printing parameters. The printing parameters directly affected measured width of extruded filaments and a wider extrusion resulted from increased T n , increased W set , and decreased V. An assumption of normal distribution appropriately represented the planar fibre orientation distribution. A direct relationship was found between fibre orientation and extrusion width: fibres were more highly oriented along the printing direction when the extruded filament was narrower. This is logical because when extruded material spreads laterally to the printing direction - to achieve wider extrusions - the polymer-fibre-composite melt flow is less aligned to the printing direction. A consistent relationship between fibre orientation and extrusion width was found even when printing conditions varied several-fold (4-fold change in V; 2-fold change in W set ; 20 °C change in T n ), highlighting the dominant influence of the extruded-filament geometry. This relationship also existed for considerably different 3D printing hardware and a different polymer (acrylonitrile butadiene styrene). A case study for tensile modulus of three different short-carbon-fibre-reinforced polymers, each with three different extrusion widths, showed how the findings related to fibre orientation can explain variation in mechanical properties for multiple materials. The results of this study allow more in-depth understanding and analysis of processing-structure-property correlations. They also highlight the crucial role of extrusion width and fibre orientation as well as the importance of characterising both direct and indirect effects of printing parameters.

Experimental and simulation investigations of the effect of hybrid GO-thermoplastic polyimide sizing on the temperature-dependent tensile behavior of short carbon fiber/polyetherimide composites

Sizing treatment of fibers is crucial to the interfacial properties and thus the mechanical properties and temperature dependent reinforcing efficiency of short fiber reinforced polymer composites. In this work, polyetherimide (PEI) composites were prepared using highly efficient injection molding technique by introducing three kinds of short carbon fibers (SCFs) treated with graphene oxide (GO), thermoplastic polyimide (TPI) and GO-TPI hybrid sizing, respectively. The effects of sizing agent on the temperature-dependent (−60 °C, 20 °C and 100 °C) tensile behavior of SCF/PEI composites were systematically investigated. The results indicate that the tensile strength of SCF/PEI composites increases with decreasing the temperature due to the higher interfacial clamping stress at low temperature. In particular, it is observed that the hybrid GO-TPI sizing leads to the optimum reinforcement effectiveness, attributing to the blocking effect of GO and the miscibility of TPI and PEI. Single fiber push-in test was conducted and corresponding numerical simulation was made to reveal the effect of GO-TPI sizing on the interfacial properties of SCF/PEI. In addition, the finite element modeling of single carbon fiber composite fragmentation with different sizing treatments was developed to clarify the temperature-dependent mechanical behavior of PEI composites based on the three types of coated SCFs.

Moisture Absorption Studies of COIR and Sisal Short Fiber Reinforced Polymer Composites

Abstract: Easy availability of natural fibre, low cost and ease of manufacturing have urged the attention of researchers towards the possibility of reinforcement of natural fiber to improve their mechanical properties and study the extent to which they satisfy the required specifications of good reinforced polymer composite for industrial and structural applications. Polymer composites made of natural fiber is susceptible for moisture. Moisture absorption in such composites mainly because of hydrophilic nature of natural fibers. Water uptake of natural fiber reinforced composites has an effect on different. Lot of researchers prepared the natural fiber reinforced composites without conducting water absorption tests; hence it is the potential area to investigate the behavior of the composites with different moisture absorption. In this research the experimental sequence and the materials are used for the study of coir and Sisal short fiber reinforced epoxy matrix composites. The coir and Sisal short fibers are made into the short fibers with 10 mm x 10 mm x 5 mm size. The Epoxy Resin-LY556(Di glycidyl ether of bi phenol) and Hardner-HYD951 (Tetra mine), the water absorption behaviors are analyzed in the coir and Sisal short fibers reinforced epoxy composites. The water absorption behaviors of the epoxy composites reinforced with the coir and sisal short fibers with 25, 30 and 35wt% were analyzed at three different water environments, such as sea water, distilled water, and tap water for 12 days at room temperature. It was observed that the composites show the high level of the water absorption percentage at sea water immersion as compared to the other water environments. Due to the water absorption, the mechanical properties of macro particle/epoxy composites were decreased at all weight percentages. Keywords: Natural fibre, Moisture absorption, Coir and sisal short fibre, Reinforced polymer composites, Water absorption behaviour Polymer matrix composite (Epoxy resin) using Coir and sisal short fibre and to study its moisture absorption behaviour