Particulate Polymer Composites Research Articles

Application of Sound Waves During the Curing of an Acrylic Resin and Its Composites Based on Short Carbon Fibers and Carbon Nanofibers.

Research into particulate polymer composites is of significant interest due to their potential for enhancing material properties, such as strength, thermal stability, and conductivity while maintaining low weight and cost. Among the various techniques for preparing particle-based composites, ultrasonic wave stimulation is one of the principal laboratory-scale methods for enhancing the dispersion of the discontinuous phase. Nevertheless, there is a scarcity of empirical evidence to substantiate the impact of stimulating materials with natural sound frequencies within the acoustic spectrum, ranging from 20 Hz to 20 kHz, during their formation process. The present work investigates the effect of acoustic stimuli with frequencies of 56, 111, and 180 Hz on the properties of an acrylic-based polymer and its discontinuous carbon-based composites. The results indicated that the stimulus frequency affects the cure time of the studied systems, with a notable reduction of 31% and 21% in the cure times of the neat polymer and carbon-nanofiber-based composites, respectively, after applying a frequency of 180 Hz. Additionally, the higher stimulation frequencies reduced porosity in the samples, increased the degree of dispersion of the discontinuous phase, and altered the composite materials' thermal, optical, and electrical behavior.

Development of hBN/natural fibres reinforced polymer composites using grey relation grade analysis for thermal and electrical applications

The objective of this work is to enhance the thermal conductivity and electrical properties of polymer hybrid composites through a systematic novel grey relation grade analysis (GRGA) optimization approach. This involves reinforcing the hybrid composites with hexagonal boron nitride (hBN) and various kinds of natural fibers or fillers. The development of a unique technology to produce multiphase composites using 2% of natural fibers or fillers such as coir fiber, rice husk filler, wood filler (WF), banana fiber (BF) and sugarcane fiber along with hBN (1, 3, 5 wt.%) particulates as reinforcements in epoxy matrix. The Taguchi L15 matrix array is utilized to fabricate interlaced composite samples via hand layup molding. Ultrasonic waves are used to ensure the uniform distribution of hBN filler into the matrix. Analysis of variance and GRGA reveal the significant results. It shows that the multiphase hybrid composites exhibit good thermal conductivity when higher content of hBN (5 wt.%) particulate for all the micro particulate polymer (MPP) composites. Multi-response optimization shows that the micro BF (2 wt.%) interlaces with hBN (5 wt.%) composite exhibits the higher thermal conductivity and electrical resistance compared to all other natural fiber interlaced composites. The aforementioned MPP composite has thermal conductivity of 1.03 W (m·K)−1 and electrical resistance of 279.88 Giga Ohms. Besides, the WF interlaced hBN (5 wt.%) composite shows the minimum dielectric constant compared to all other natural fiber composites. This desirable result is caused by the proper dispersion of hBN with the matrix which encourages interlocking with the fiber and the matrix. Maximum electrical resistance is observed for composite containing 5 wt.% of h-BN and 2 wt.% of BF. The developed MPP composite could be used in heat shields, electrical insulation components, and interior automotive components like dashboards, luggage compartments and interior walls.

The strength of particulate polymer composite: A new insight on crystallinity and structural morphology

AbstractWe have demonstrated the significant impact of high density polyethylene (HDPE) crystallinity and structural morphology on the tensile strength of 60/40 NR/HDPE polymer blend composites. Introducing nanoscale Boron Carbide (B4C‐type compounds) fillers noticeably changed the crystallinity and morphology of HDPE, leading to the formation of fine spherulites. Conversely, microscale fillers mainly impacted the crystallinity while preserving a coarse architecture. Although both composites experienced a reduction in crystallinity, the nanocomposites outperformed microcomposites and even the pristine polymer blend in terms of strength, implying the effect of a fine structure. The nanocomposite with a filler loading of 2 wt% exhibited the highest strength. This investigation provides novel insights into the interplay between crystallinity, HDPE structure, and the size of fillers, which heavily influence the strength of polymer blend composites.

Effective Properties for the Design of Basalt Particulate-Polymer Composites.

In this study, preliminary simulations were performed to manufacture thermoplastic composites that can be processed by injection. For analysis, a basalt particulate-polymer composite model was manufactured and its elastic modulus, shear modulus, thermal expansion coefficient, and thermal conductivity were predicted using finite-element analysis (FEA) and micromechanics. Polypropylene (PP), polyamide 6, polyamide 66, and polyamide (PA) were employed as the polymer matrix, with the variations in their properties investigated based on the volume fraction of basalt. The polymer-basalt composite's properties were analyzed effectively using FEA and the micromechanics model. FEA was performed by constructing a 3D model based on the homogenization technique to analyze the effective properties. The micromechanics model was analyzed numerically using the mixture rule, and the Mital, Guth, and Halpin-Tsai models. As a result, it is best to analyze the effective properties of polymer-basalt composites using the Halpin-Tsai model, and it is necessary to conduct a comparative analysis through actual experiments. In the future, actual composite materials need to be developed and evaluated based on the findings of this study.

Influence on Elastic Wave Propagation Behavior in Polymers Composites: An Analysis of Inflection Phenomena

Particulate polymer composites (PPCs) are widely applied under different elastic wave loading conditions in the automobile, aviation, and armor protection industries. This study investigates the elastic wave propagation behavior of a typical PPC, specifically a Cu/poly (methyl methacrylate) (PMMA) composite, with a wide range of particle contents (30-65 vol. %) and particle sizes (1-100 μm). The results demonstrate an inflection phenomenon in both the elastic wave velocity and attenuation coefficient with increasing volume content. In addition, the inflection point moves to the direction of low content with the increase in particle size. Notably, the elastic wave velocity, attenuation, and wavefront width significantly increased with the particle size. The inflection phenomenon of elastic wave propagation behavior in PPCs is demonstrated to have resulted from particle interaction using the classical scattering theory and finite element analysis. The particle interaction initially intensified and then reduced with increasing particle content. This study elucidates the underlying mechanism governing the elastic wave propagation behavior of high particle content PPCs and provides guidelines for the design and application of wave-absorbing composites.

Effect of Polymer Matrix on Inelastic Strain Development in PI- and PEI-Based Composites Reinforced with Short Carbon Fibers under Low-Cyclic Fatigue

Since the inelastic strain development plays an important role in the low-cycle fatigue (LCF) of High-Performance Polymers (HPPs), the goal of the research was to study the effect of an amorphous polymer matrix type on the resistance to cyclic loading for both polyimide (PI)- and polyetherimide (PEI)-based composites, identically loaded with short carbon fibers (SCFs) of various lengths, in the LCF mode. The fracture of the PI and PEI, as well as their particulate composites loaded with SCFs at an aspect ratio (AR) of 10, occurred with a significant role played by cyclic creep processes. Unlike PEI, PI was less prone to the development of creep processes, probably because of the greater rigidity of the polymer molecules. This increased the stage duration of the accumulation of scattered damage in the PI-based composites loaded with SCFs at AR = 20 and AR = 200, causing their greater cyclic durability. In the case of SCFs 2000 µm long, the length of the SCFs was comparable to the specimen thickness, causing the formation of a spatial framework of unattached SCFs at AR = 200. The higher rigidity of the PI polymer matrix provided more effective resistance to the accumulation of scattered damage with the simultaneously higher fatigue creep resistance. Under such conditions, the adhesion factor exerted a lesser effect. As shown, the fatigue life of the composites was determined both by the chemical structure of the polymer matrix and the offset yield stresses. The essential role of the cyclic damage accumulation in both neat PI and PEI, as well as their composites reinforced with SCFs, was confirmed by the results of XRD spectra analysis. The research holds the potential to solve problems related to the fatigue life monitoring of particulate polymer composites.

An automated software development for analysis of the morphological-tensile property relationship in egg shell bio-based particulate composites using machine learning algorithms

This work explored the importance of quantitative observation through imaging methods of optical and electron microscopies on the mechanical properties of particulate polymeric composites. Egg shell powder (ESP) reinforced polypropylene carbonate (PPC) polymeric composites with different filler weight percentage (wt.%) from 1 to 5 wt.% were considered. A cost-effective Image Analysis Software (IAS) was developed to extract black particles from the original optical images. During this process, the optimal image can be reproduced based on its originality by controlling the threshold values from 0.1 to 0.6 in real time situation. Using one-dimensional (1D) Gaussian distribution analysis, the authentication of the particle distribution data was studied and linked to the tensile strength of the composites. The mean value of the particle area collected from the left and right side of the scattered curves has a significant effect on the tensile strength of the composites. The proposed model was validated by comparing the predicted statistical results with the measured tensile strength for different wt.% of ESP composites. From the results obtained, a close agreement of 99% accuracy was observed between the experimental results and the proposed model for the tensile strength of the composites. The innovative study provides more practical and quantitative knowledge on improved particulate polymeric composites, in addition to the detection of failure processes through optical/electron microscopic examination of images. Evidently, the proposed cost effective, accurate and less stressful model can be employed by several composite-based industries to correlate the tensile strengths of particulate polymeric composites with their morphological properties.

Experimental data-driven uncertainty quantification for the dynamic fracture toughness of particulate polymer composites

This paper presents an experimental investigation supported by data-driven approaches concerning the influence of critical stochastic effects on the dynamic fracture toughness of glass-filled epoxy composites using a computationally efficient framework of uncertainty quantification. Three different shapes of glass particles are considered including rod, spherical and flaky shapes with coupled stochastic variations in aspect ratio, dynamic elastic modulus and volume fraction. An artificial neural network based surrogate assisted Monte Carlo simulation is carried out here in conjunction with advanced experimental techniques like digital image correlation and scanning electron microscopy to quantify the uncertainty and sensitivity associated with the dynamic fracture toughness of composites in terms of stress intensity factor under dynamic impact. The study reveals that the pre-crack initiation time regime shows the most prominent effect of uncertainty. Additionally, rod shape and the aspect ratio are the most sensitive filler type and input parameter respectively for characterizing dynamic fracture toughness. Here the quantitative results based on large-scale data-driven approaches convincingly demonstrate using a computational mapping between the stochastic input and output parameter spaces that the effect of uncertainty gets pronounced significantly while propagating from the compound source level to the impact responses. Such outcomes based on experimental data essentially bring us to the realization that quantification of uncertainty is of utmost importance for developing a reliable and practically relevant inclusive analysis and design framework for the dynamic fracture of particulate composites. With limited literature available on the determination of fracture toughness considering inertial effects, the present work demonstrates a novel and insightful experimental approach for uncertainty quantification and sensitivity analysis of dynamic fracture toughness of particulate polymer composites based on surrogate modeling.

Rheology of particulate suspensions with non-Newtonian fluids in capillaries.

Particulate suspensions occur in situations from blood flow to slurries in drilling applications. Existing investigations of these suspensions generally concentrate on the impact of particle volume fraction for suspensions in Newtonian fluids under free-flow conditions. Recently, particulate-polymer composites have been used in additive manufacturing (AM). Here, the polymer becomes a shear-thinning non-Newtonian fluid during extrusion, creating a particulate suspension. Motivated by the challenges in AM of particulate composites, this study investigates the rheology of suspensions of micrometre-sized particles in shear-thinning silicone while extruded through AM-scaled nozzles (millimetre-scale diameters). The suspensions were observed to follow a power-law behaviour and their rheology was investigated through the measured flow consistency () and behaviour () indices. The impact of the particle volume fraction () and the ratio () of the capillary inside diameter to the particle diameter on both indices were measured. was found to be only impacted by the suspension fluid type and . was found to be constant at large , but decreased and then increased to infinity with decreasing. Based on its behaviour, was categorized into two conditions and analysed separately with semi-empirical models. The impact of particle size distribution was also investigated.

Mechanical properties of particulate organic natural filler-reinforced polymer composite: A review

Considering the cost in utilization of organic natural filler as an alternative to manmade fiber and mineral inorganic filler-reinforced polymer composite is of great interest. The main reasons for using natural fillers are to reduce the dependence on petroleum-based, nonrenewable resources and are also a smarter use of environment and financial resources. Only limited research works have been done on the mechanical properties, such as tensile, flexural, and impact on particulate organic natural filler-reinforced polymer composite. The effect of particle size, particle loading, and chemical treatment on mechanical properties of organic natural filler-reinforced polymer composites is reviewed and discussed. The results show that a smaller particle size with an aspect ratio higher than its critical value provided better mechanical properties. With the assumption of good adhesion between the particle filler and matrix, mechanical properties increased with volume fraction until it reached its optimum condition or failure. Effective chemical treatments would improve the homogeneity and adhesion of the filler/matrix, thus enhancing the mechanical properties of the composites.

Selection of resin and aggregates for particulate polymer concrete machine tool structures-A review

Precision machine tools are the most essential machines in manufacturing sector. Performance of precision machines in terms of high accuracy and high productivity is depending on the static, dynamic and thermal characteristics of the machine tools. Use of particulate polymer composites in machine tool applications improves the dynamic characteristics due to their high damping capacity than conventional materials like cast-iron. Literature survey conducted for selecting aggregates, resins and other fillers for polymer matrix composites for machine tool structures presented in this article. Aggregate types and sizes, effective resin content and other filler materials will influence the mechanical and thermal characteristics of machine tool structures. Aggregate sizes will influence the presence of air voids in prepared composite which influences the damping capacity. Bond strength between resin and particulates depend on effective resin content. Type and volume fraction of resin, hardener chemical reaction impacts the curing time. Manufacturing of machine tools is based on stiffness, strength and damping capacity is criteria for effective design. Particulate polymer concrete development is crucial to achieve required characteristics. In this article design challenges and solutions for designers to develop particulate composites for machine tool structures have been presented by review.

A study on hardness and thermal properties of fibre based particulate polymer composites

An experimental study has been conducted to determine the hardness properties and to analyse the thermal behaviour of green polymer composites with a particulate reinforcing material of a particular size range. These composites are obtained by reinforcing bagasse particulates (fibre that remains after the juice has been extracted from the sugarcane) and coir particulates (fibre obtained from the outer shell of a coconut), in different percentage compositions, both individually and as a combination, into the epoxy resin matrix, with and without a hardener as a catalyst. The fabricated composites are subjected to a Brinell Hardness test to evaluate it’s hardness property, and also to a Thermo-gravimetric analysis in so as to carry out thermal analysis and check the burn out of the sample specimen.

Factors affecting the solid particle erosion of environment pollutant and natural particulate filled polymer composites—A review

Solid particle erosion of polymer matrix composites filled with naturally available and environment pollutant fillers have not been studied to the same level as for metals or ceramics and is focus of the present study. In this article, review of the research associated with the erosion response of polymer composites is presented. Particulate polymer composites are employed extensively owing to their enhanced specific properties and tribological response. Particulate filler particles such as environmental pollutants and naturally available ones need to be effectively incorporated in utilitarian applications so as to reduce land fill burden issues and other specific problems. Nevertheless, adequate data is not available in review articles on the erosion of fillers that are environment pollutants and thereby an ample amount of research can be carried out in this regard. Erosion behavior of polymer composites in particular has gained a lot of attention among researches in the recent decade. Viability of incorporating various fillers in polymer matrix for erosion resistive applications needs to be assessed so that the potential of these composites can be well understood. Therefore in this study, erosion response of polymer composites reinforced with fillers is reviewed with a focus on input parameters (impact velocity, impingement angle and erodent properties) and material properties (density).

Artificial neural network technique to predict dynamic fracture of particulate composite

In this paper, the artificial neural network technique using a multi-layer perceptron feed forward scheme was used to model and predict the mode-I fracture behaviour of particulate polymer composites when subjected to impact loading. A neural network consisting of three-layers was employed to develop the network. Artificial neural network was constructed using six input parameters such as shear wave speed ( CS), density ( D), elastic modulus ( Ed), longitudinal wave speed ( CL), volume fraction ( Vf) and time ( t). The influence of input parameters on the output stress intensity factor and crack-initiation fracture toughness were found to be in the order of t > CS > D > Ed > CL > Vf. The degree of accuracy of prediction was 92.7% for stress intensity factor. In this regard, artificial neural network can be used in the modelling and prediction of fracture behaviour of particulate polymer composites under impact loading.

A multi-scale deformation analysis for a particulate composite at the rigidity threshold

The rigidity threshold in a two-phase soft-rigid material is linked to the formation of networks of the rigid phase, which supports a substantial fraction of overall load. Metal filled particulate polymer composites were used to characterize rigidity transition experimentally, as metals and polymers have contrasting mechanical properties enabling easy detection of the rigidity transition. A detailed analysis of the load transferring mechanisms was performed using in-situ synchrotron X-ray diffraction to directly verify the compressive loading of the rigid metal particles at a microscopic scale. Elastic buckling of the force chains was attributed to the non-affine deformation at the rigidity threshold at room temperature, using digital image correlation (DIC) technique at a mesoscopic length scale. However, beyond the glass transition temperature of the polymer, the deformation was homogeneous due to the low viscosity and high compressibility of the polymer and the absence of buckling of force chains. Macroscopically, an increasing Poisson ratio was observed as a function of elastic strains, which is quite unique for these composites. The criteria for having increasing Poisson ratio is discussed based on elastic buckling of force chains and polymer incompressibility, and a link is proposed between deformation at microscopic, mesoscopic and macroscopic scale for the composite.

Effect of aspect ratio on dynamic fracture toughness of particulate polymer composite using artificial neural network

The present study discusses about the effect of the aspect ratio of the fillers on the fracture toughness of the glass-filled epoxy composites under impact loading. Three different kinds of fillers (spheres, flakes and rods) were used with different volume fractions (5%, 10% and 15%). Experimental results for Stress Intensity Factor (SIF) were obtained using a gas gun setup and a high speed camera. Further experimental investigation was done using fractographs obtained from Scanning Electron Microscope (SEM). Then the potential of using Artificial Neural Network (ANN) in predicting the effect of filler shape on the fracture behavior is studied. The framework of Multi-Layer Perceptron (MLP) feed forward network was used to predict the SIF history using four input parameters viz. time, dynamic elastic modulus, aspect ratio and volume fraction of the glass fillers. Experimental results of fracture test under impact loading were fed to train the ANN network and later the predicted results were compared with the experimental ones. Owing to the fact that predicted values had an accuracy of 91%, crack initiation toughness was predicted corresponding to the intermediate values of aspect ratio for which the experiments were not performed. Among the four input parameters, aspect ratio (largest/shortest dimension) was found to be the most important parameter in the prediction of SIF after time, followed by the dynamic modulus and volume fraction. The significance of aspect ratio lies in increasing the surface area to volume ratio which is responsible for the interfacial strength between the matrix and the filler and hence affects the fracture toughness of the overall composite material.

Evaluation of tensile and flexural properties of foundry slag reinforced particulate polymer composite

Mechanical behaviour of unsaturated polyester resin reinforced with foundry slag is used for the composite materials has been studied to determine various important parameters, such as tensile and flexural properties by using moulding process. 75 µm level particle sizes of fine powdered foundry slag are used for sample preparation. Concentration of powdered slag is varied from 5%, 7.5%, 15% by weight in the polyester resin. The composite test specimens are prepared using moulding process as per ASTM D638 for tensile test and ASTM D 790 for Flexural test.

Dynamic fracture toughness index: a new integrated methodology for mode-I fracture behaviour of polymer composite under impact loading

In the current work, a new term called dynamic fracture toughness index () for mode-I (crack opening mode) configuration is presented. This index is useful in determining the dynamic fracture toughness under different strain-rate conditions. is evolved based on dimensional analysis of the parameters that govern high strain-rate impact loading conditions and offers an integrated methodology to characterize dynamic fracture behaviour which is validated for particulate polymer composites (PPCs). The validity of the developed methodology is corroborated with the dynamic fracture experimental data for varying volume fraction of glass fibers and as well as predicting the dynamic fracture toughness with limited number of experiments.

Theoretical modeling of the temperature dependent tensile strength for particulate-polymer composites

In this work, a temperature dependent tensile strength model for particulate-polymer composites was developed. The combined effects of temperature, particle volume fraction, particle radius, the evolution of interfacial bonding strength and polymer matrix strength with temperature are considered in this theoretical model. It was verified by comparison with the available tensile strength of particulate-polymer composites at different temperatures. Good agreement between the theoretical predictions and experimental results is obtained, which demonstrates the reasonability and applicability of the model. Additionally, we discussed the comparisons between the proposed model and the commonly used Pukanszky's model, and performed the influencing factors analysis regarding the temperature dependent tensile strength of particulate-polymer composites in detail. This study offers a reasonable method to predict the tensile strength of particulate-polymer composites at different temperatures, which could replace phenomenological models that do not have predictive capability. Meanwhile, some useful insights concerning the material evaluation, strengthening, and optimization are obtained.

Effect of Particle Mass Fraction on the Multiscale Dynamic Failure Behavior of Particulate Polymer Composites

In this study, the effect of particle mass fraction on the dynamic multiscale behavior of particulate polymer composite is investigated. High spatiotemporal resolution digital image correlation-based experiments are conducted to understand the local deformation and the failure mechanisms. The deformation fields at different length scales are compared to each other to understand the link between macroscaleand mesoscale behavior. All the experiments are carried out on polymer bonded sugar, a well-known mechanical simulant of polymer bonded explosives, with varying particle mass fraction. The transition of the soft binder dominated stress-strain behavior to a quasi-brittle type behavior was observed as the particle mass fraction is increased. The mesoscale experiments show that in samples with lower mass fraction, the deformation is mainly controlled by the binder. Whereas, at a higher mass fraction, the crystals are engaged in the load transferring process through the formation of force chain in the material. The formation of the force chain causes stress concentration followed by a brittle failure mode.