Short Fiber-reinforced Polymer Research Articles

Fully automated measurement of the spatial distribution of both fibre length and orientation from micro-CT images of short fibre reinforced polymers

The morphology of Short Fibre Reinforced Polymers (SFRPs) plays a fundamental role in determining their stiffness, strength and fracture behaviour. Measurements tools for the analysis of their microstructure are therefore of paramount importance. To this end, a fully automated algorithm able to segment single fibres from X-ray micro-computed tomography images was developed. This method was tailored to reconstruct the microstructure of large volumes of material; in particular, to acquire fibre length, position and orientation, even dealing with low-resolution images. The algorithm was tested on different specimens of short glass fibre-reinforced polyamide and it was validated comparing the fibre orientation with the one obtained with commercial software analysis and the fibre length with the experimentally determined one. Therefore, the proposed algorithm allows to easily identify microstructural trends without requiring the usual complex evaluating procedures.

Prediction of Flexural Properties of Additively Manufactured Short Fiber-Reinforced Polymer Composite Parts

Prediction of Flexural Properties of Additively Manufactured Short Fiber-Reinforced Polymer Composite Parts

Short Fiber-Reinforced Polymer Polyamide 6 Lugs and Selective Laser-Melted Ti-6Al-4V Bushing Contact Cohesive Zone Model Mode II Parameters’ Evaluation

This paper discusses an approach to estimating the parameters of the cohesive zone model (CZM) by mode II by extruding the bushing along the lug axis. This method of evaluation requires small samples, which is particularly relevant when investigating short fiber-reinforced polymers (SFRPs) with additively manufactured embedded elements. Adhesion is investigated on the example of 30% carbon fiber-reinforced polyamide-6 molded to Ti-6Al-4V (VT6) selective laser-melted (SLM) alloy bushing in cases of a roughness Ra = 2.66 μm (vibratory finishing), Ra = 8.79 μm (sandblasting), and Ra = 10.02 (directly from SLM). The values of the maximum equivalent tangential contact stress were in a range from 1.1 MPa to 9.5 MPa, while the critical fracture energy for tangential slip was estimated at 15 N/mm for all cases. Experimental validation of the obtained CZM mode II was carried out by evaluating the load-carrying capacity of the lugs with different bushings. In both the experiment and the calculation, greater bushing roughness provides greater lug load-bearing capacity. The ribbed bushings added significant strength in the experiments, which confirmed the importance of considering the tangential mode in the contact model. The presented models can be used for the preliminary evaluation of short fiber-reinforced polyamide-6 parts with titanium-embedded elements bearing capacity.

Improving the Accuracy of the Evaluation Method for the Interfacial Shear Strength of Fiber-Reinforced Thermoplastic Polymers through the Short Beam Shear Test.

Short fiber-reinforced thermoplastic polymers (SFRTPs) are commonly used in various molding methods due to their high specific elasticity and strength. To evaluate the interfacial strength, several determination methods have been proposed, including the interfacial shear strength (IFSS). In previous research, an IFSS evaluation method based on the short beam shear method was proposed. However, this method is only applicable to micrometer-sized fibers with high stiffness levels that are not easily bent. When utilizing cellulose fiber, the interfacial shear strength (IFSS) results frequently exhibit significant deviations. To tackle this issue, we suggest an enhanced experimental technique that employs beam-shaped specimens with welding points based on the short beam shear test. Furthermore, we conducted a three-dimensional analysis of the original method to determine the fiber orientation angle and IFSS. The outcomes were compared with previously reported determinations. The IFSS achieved through the novel method proposed in this paper exhibits high precision and reliability, rendering it suitable for use with soft and flexible fibers.

Modeling the high‐cycle fatigue crack initiation in short fiber reinforced polymers with notches

AbstractIn this work, a new approach is proposed for predicting the life to crack initiation in Short Fiber Reinforced Polymers (SFRPs) under cyclic loadings in the presence of notches, cut‐outs and stress concentrations. A multiscale model is presented, encompassing two length scales for correlating the applied loads to the local stress fields in the matrix. These are used for defining an effective stress for predicting the life to crack initiation, accounting for the simultaneous influence of the notch geometry, the fiber content, orientation and distribution. The criterion was successfully validated on a large set of experimental results.Highlights A new model is proposed for predicting fatigue crack initiation in notched SFRPs The model is based on a multiscale procedure Prediction is based on local matrix stresses averaged in a structural volume The model accounts for fiber orientation, distribution and notch geometry

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.

Topology Optimization and Efficiency Evaluation of Short-Fiber-Reinforced Composite Structures Considering Anisotropy

The current study aims to develop a methodology for obtaining topology-optimal structures made of short fiber-reinforced polymers. Each iteration of topology optimization involves two consecutive steps: the first is a simulation of the injection molding process for obtaining the fiber orientation tensor, and the second is a structural analysis with anisotropic material properties. Accounting for the molding process during the internal iterations of topology optimization makes it possible to enhance the weight efficiency of structures—a crucial aspect, especially in aerospace. Anisotropy is considered through the fiber orientation tensor, which is modeled by solving the plastic molding equations for non-Newtonian fluids and then introduced as a variable in the stiffness matrix during the structural analysis. Structural analysis using a linear anisotropic material model was employed within the topology optimization. For verification, a non-linear elasto-plastic material model was used based on an exponential-and-linear hardening law. The evaluation of weight efficiency in structures composed of short-reinforced composite materials using a dimensionless criterion is addressed. Experimental verification was performed to confirm the validity of the developed methodology. The evidence illustrates that considering anisotropy leads to stiffer structures, and structural elements should be oriented in the direction of maximal stiffness. The load-carrying factor is expressed in terms of failure criteria. The presented multidisciplinary methodology can be used to improve the quality of the design of structures made of short fiber-reinforced composites (SFRC), where high stiffness, high strength, and minimum mass are the primary required structural characteristics.

A numerical multi-scale method for analyzing the rate-dependent and inelastic response of short fiber reinforced polymers: Modeling framework and experimental validation

This research presents a numerical multi-scale approach that efficiently addresses the inelastic and time-dependent mechanical response of short fiber reinforced polymers (SFRPs) under monotonic loading conditions by linking the mechanical analysis from microscale analysis to a continuum model. To do so, first, the mechanical performance of a recently suggested unit cell, considering the intrinsic mechanical characteristics of both fiber and matrix, is studied to address the inelastic and rate-dependent mechanical behavior of completely aligned SFRPs. Then, the evaluated mechanical response is linked to the Hill's plasticity and two-layer viscoplastic (TLVP) models to represent the anisotropic mechanical response of SFRPs. Furthermore, an easy-to-use multi-step homogenization process is considered to numerically incorporate the influence of fiber misalignments. Finally, the suggested multi-scale technique is thoroughly validated at different strain rates, by using experimental observations of short fiber composites with high volume fraction and direct FE simulations of RVEs with complex microstructures.

Prediction Models of Mechanical Properties of Jute/PLA Composite Based on X-ray Computed Tomography.

The tensile strength and modulus of elasticity of a jute/polylactic acid (PLA) composite were found to vary nonlinearly with the loading angle of the specimen through the tensile test. The variation in these properties was related to the fiber orientation distribution (FOD) and fiber length distribution (FLD). In order to study the effects of the FOD and FLD of short fibers on the mechanical properties and to better predict the mechanical properties of short-fiber composites, the true distribution of short fibers in the composite was accurately obtained using X-ray computed tomography (XCT), in which about 70% of the jute fibers were less than 300 μm in length and the fibers were mainly distributed along the direction of mold flow. The probability density functions of the FOD and FLD were obtained by further analyzing the XCT data. Strength and elastic modulus prediction models applicable to short-fiber-reinforced polymer (SFRP) composites were created by modifying the laminate theory and the rule of mixtures using the probability density functions of the FOD and FLD. The experimental measurements were in good agreement with the model predictions.

Evaluation of wall thickness effect on tensile properties of injected short fiber reinforced polymers

The aim of this paper is to analyze the effect of increasing the wall thickness in injection molded short fiber reinforced polymer (SFRP) parts, focusing mainly on tensile strain and tensile strength, as these are the most used characteristics for structural analyses. The phenomena that makes this evaluation of interest has to do with the microstructure of SFRP injection molded parts and how the fibers get oriented along the flow direction at superficial depths with an increasingly less aligned structure down to the mid plane/surface. By increasing the injected part wall thickness the ratio between the thicknesses of highly oriented layers and poorly oriented layers decreases, thus the tensile properties of the material should suffer a decrease as well.In order to study the mentioned hypothesis two types of materials were tested, PPA GF33 and PPS GF40, dog-bone type specimens were cut at different angles to the flow direction from injection molded plates of 2 and 3 mm wall thickness values. The tensile strains were recoded using a video extensometer and material data was calibrated using Digimat MX reverse engineering module and Ansys properties homogenization tool and finite element solver.

Visualizing Fiber End Geometry Effects on Stress Distribution in Composites Using Mechanophores

Localized stress concentrations at fiber ends in short fiber-reinforced polymer composites (SFRCs) significantly affect their mechanical properties. Our research targets these stress concentrations by embedding nitro-spiropyran (SPN) mechanophores into the...

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

Probabilistic physics-guided transfer learning for material property prediction in extrusion deposition additive manufacturing

We introduce the concept of physics-guided transfer learning to predict the thermal conductivity of an additively manufactured short-fiber reinforced polymer (SFRP) using micro-structural characteristics extracted from tensile tests. Developing composite manufacturing digital twins for SFRP composite processes like extrusion deposition additive manufacturing (EDAM) require extensive experimental material characterization. Even the same material system printed on different EDAM systems can result in significant changes to the printed micro-structure, affecting the mechanical and transport properties. This, in turn, makes characterization efforts expensive and time-consuming. Therefore, the objective of the paper is to address this experimental bottleneck and use prior information about the material manufactured in one extrusion system to predict its properties when manufactured in another system. To enable this framework, we assume that changes in properties of the same material when manufactured in different systems arise solely due to microstructural changes. To that end, we present a Bayesian framework that can transfer thermal conductivity properties across extrusion deposition additive manufacturing systems. While we discuss the transfer of thermal conductivity properties, the development is such that the framework can be used for the transfer of other properties that depend on microstructural characteristics defined in the manufacturing process. These include the coefficient of thermal expansion, viscoelastic properties, etc. The framework begins by using thermal conductivity data of the composite printed in one extrusion system to probabilistically infer the constituent thermal properties of the fiber and the polymer. Next, by conducting limited tensile tests of the same material printed in another extrusion system, we infer its orientation tensor. Finally, the inferred constituent thermal conductivity properties and the inferred orientation tensor are coupled using a micromechanics model to predict the thermal conductivity properties of the composite printed in the second extrusion system. We experimentally verify the predictions and show that our method provides a reliable framework for transferring material properties while accounting for epistemic and aleatory uncertainties.

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.

A machine learning based prediction of elasto-plastic response of a short fiber reinforced polymer (SFRP) composite

Several homogenization techniques are available in the literature to compute the mechanical response of the short fiber-reinforced polymer (SFRP) composites. However, in some cases, the complex modeling of the SFRP makes it computationally expensive. In this study, an artificial neural network (ANN) is developed to predict the elasto-plastic response of an SFRP. The datasets for training the ANN model are obtained from Mori-Tanaka mean-field homogenization using the commercial software Digimat. The elasto-plastic response predicted by the ANN model is compared with the experimental results and with different homogenization schemes reported in the literature. Additionally, the effect of significant parameters on the response of the SFRP is extensively studied using the ANN model.

Thermo-viscoelastic multiscale homogenization of additively manufactured short fiber reinforced polymers from direct microstructure characterization

Short fiber reinforced polymers (SFRPs) processed by Fused Filament Fabrication (FFF) show great promise for applications requiring high weight-specific mechanical properties, such as aerospace. However, the unique morphology created by the printing process and the relationship between processing parameters and part behavior is not sufficiently well understood, making structural analysis a largely empirical task. The limited ability to predict the effective viscoelastic properties and their thermal dependence is a problem from a mechanical design standpoint and is an obstacle to the widespread adoption of SFRPs made by FFF. In this work, those properties are evaluated using high-resolution imaging of physical samples, enabling the accurate prediction of time-dependent stress–strain relations across different temperatures. Multiscale homogenization is performed considering individual components (fibers, pores, matrix) at the microscale, and the regular pattern created by the printing process at the mesoscale. The efficiency of the Fast Fourier Transform (FFT) based homogenization procedure is leveraged to allow for large Representative Volume Element (RVE) sizes, which are required due to the complexity of real microstructures. The predicted thermo-viscoelastic behavior is validated against creep-recovery data of specimens printed from 30 wt% short carbon fiber reinforced poly-ether-ether ketone (PEEK), and the experimental data can be predicted within 3% mean relative error for axial loading, both at 21°and 120 °C. Imaging data and pre-processed RVEs as well as original source code are made publicly available.

A modified energy‐based fatigue parameter for short fiber reinforced polymers: Performance analysis with varying thicknesses, load ratios, and fiber orientations

AbstractThe durability of short fiber reinforced polymers (SFRPs) is affected by several variables. Accurate unifying fatigue parameters are thus essential for an efficient characterization campaign. This research investigated the fatigue behavior of a Polyphtalamide PA6T/6I reinforced with 50% of glass fibers. Two thicknesses (1.6 and 3 mm), two orientations from the injection molding direction (0° and 90°), and three load rations (−0.5, 0.1, 0.5) were investigated. A new fatigue parameter, called alternating energy density (AED), was presented; its ability to correlate the fatigue tests results was compared to other known fatigue parameters—cyclic mean strain rate and cyclic creep energy density. All the fatigue parameters were found to be independent of specimen thickness. Furthermore, the prediction of the fatigue lifetime based on AED showed greater accuracy with respect to the other known investigated methods. AED could thus be used for accurate and efficient lifetime prediction of SFRPs.

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.

Impact-Dissipating Capacity of Fiber-Reinforced Polymer Samples, Fabricated by Fused Filament Fabrication

The use of additive manufacturing for the fabrication of sacrificial cladding is becoming increasingly popular as it facilitates the production of complex yet space-saving protective structures. Despite this, the effect of several structural parameters on their capacity to mitigate high-velocity impacts remains elusive. Toward this end, the shock-mitigating capacity of various short fiber-reinforced polymer samples was evaluated regarding impact velocity and mass (raging from 1 to 8.3 m/s and 5.5 to 7.5 Kg, respectively). Among the assessed parameters were peak force (measured to vary by up to 46.6%), max. and mean deceleration values (with max. differences documented at 29.5% and 48.2%, respectively) and cushion factor. As expected, the progressive crushing modes differed significantly across the spectrum of the tested samples. Structural failure involved the growth of inter- and intra-laminar cracks, fiber-matrix de-bonding and de-lamination, which was dependent on equivalent pore volume fraction and compressive strength. Increasing infill density led in most cases to higher peak forces during impact, as did the deposition of more solid peripheral layers, with the latter producing a superior deceleration plateau. Evaluated collectively, the results indicate that an infill density of 37% with 4 solid external (protective) layers exhibited the superior impact response among the tested samples.