Particulate Composite Materials Research Articles

Bruggeman homogenization of a particulate composite material comprising truncated spheres and spheroids

Closed-form expressions were established for depolarization dyadics for a truncated sphere and a truncated spheroid, both electrically small, immersed in a uniaxial dielectric ambient medium. These depolarization dyadics were used to develop the Bruggeman homogenization formalism to predict the relative permittivity dyadic of a homogenized composite material (HCM) arising from a randomly distributed mixture of oriented particles shaped as truncated spheres and spheroids. Unlike other homogenization formalisms, most notably the Maxwell Garnett formalism, the Bruggeman formalism is not restricted to composites containing dilute volume fractions of constituent particles. Numerical investigations highlighted the anisotropy of the HCM and its relation to the shapes of the constituent particles and their volume fractions. Specifically, greater degrees of HCM anisotropy arise from constituent particles whose shapes deviate more from spherical, especially for mid-range volume fractions.

Damage sensing in multi-functional nanocomposite polymer bonded energetics with embedded multi-walled carbon nanotube sensing networks

Abstract This experimental investigation evaluates the strain and damage sensing abilities of multi-walled carbon nanotube (MWCNT) networks embedded in the binder phase of polymer-bonded energetics (PBEs). PBEs are a special class of particulate composite materials that consist of energetic crystals bound by a polymer matrix, wherein the polymer matrix serves to maintain the composite’s shape and form. The structural health monitoring (SHM) approach presented in this work exploits the piezoresistive properties of the distributed MWCNT networks. Major challenges faced during such implementation include the low binder concentrations of PBEs, the presence of conductive/non-conductive particulate phases, the high degree of heterogeneity in the PBE microstructure, and achieving the optimal MWCNT dispersion. In this study, ammonium perchlorate (AP) crystals as the oxidizer, Aluminum grains as the metallic fuel, and Polydimethylsiloxane (PDMS) as the binder are used as the constituents for fabricating PBEs. To study the effect of each constituent on the MWCNT network’s SHM abilities, various materials systems are comprehensively studied: MWCNT/PDMS materials are first evaluated to study the binder’s electromechanical response, followed by AP/MWCNT/PDMS to assess the impact of AP addition, and finally, AP/AL/MWCNT/PDMS to evaluate the impact of adding conductive aluminum grains. Compression samples (ASTM D695) were fabricated and subjected to monotonic compression. Electrical resistance is recorded in conjunction with the mechanical test via an LCR meter. Gauge factors relating to the change in normalized resistance to applied strain are calculated to quantify the electromechanical response. MWCNT dispersions and mechanical failure modes are analyzed via scanning electron microscopy imaging of the fracture surfaces. Correlations between the electrical behavior in response to the mechanical behavior are presented, and possible mechanisms that influence the electromechanical behavior are discussed. The results presented herein demonstrate the successful ability of MWCNT networks as SHM sensors capable of real-time strain and damage assessment of PBEs.

Multiscale modelling of particulate composites with spherical inclusions

AbstractThis paper presents a novel and effective strategy for modelling three-dimensional periodic representative volume elements (RVE) of particulate composites. The proposed method aims to generate an RVE that can represent the microstructure of particulate composites with hollow spherical inclusions for homogenization (e.g., deriving the full-field effective elastic properties). The RVE features periodic and randomised geometry suitable for the application of periodic boundary conditions in finite element analysis. A robust algorithm is introduced following the combined theories of Monte Carlo and collision driven molecular dynamics to pack spherical particles in random spatial positions within the RVE. This novel technique can achieve a high particle-matrix volume ratio of up to 50% while still maintaining geometric periodicity across the domain and random distribution of inclusions within the RVE. Another algorithm is established to apply periodic boundary conditions (PBC) to precisely generate full field elastic properties of such microstructures. Furthermore, a user-friendly automatic ABAQUS CAE plug-in tool ‘Gen_PRVE’ is developed to generate three-dimensional RVE of any spherical particulate composite or porous material. Gen_PRVE provides users with a great deal of flexibility to generate Representative Volume Elements (RVEs) with varying side dimensions, sphere sizes, and periodic mesh resolutions. In addition, this tool can be effectively utilized to conduct a rapid mesh convergence study, an RVE size sensitivity study, and investigate the impact of inclusion/matrix volume fraction on the solution. Lastly, examples of these applications are presented.

Investigation for the optimum selection of hybrid AA2024 – ceramic particulate alloy composite materials prepared through stir casting using the hybrid AHP-TOPSIS MCDM technique

ABSTRACT In most of the manufacturing industries, the selection of optimal material among the finite available alternatives becomes more difficult with the more required performance criteria to satisfy certain objectives. In such scenarios, Multi-Criteria-Decision-Making (MCDM) techniques, such as hybrid AHP (Analytical hierarchy process) – TOPSIS (Technique for Order Preferences by Similarity to Ideal Solution) and others assist in quantitative decision-making by accounting for qualitative human judgments. The present work illustrates the application of a hybrid AHP-TOPSIS technique to the ranking of alloy composites. The properties defining data such as physical, mechanical, thermal, thermo-mechanical, etc. are weighted using the AHP technique; thereafter, relative weights are used to rank the alloy composite compositions by the TOPSIS method. The compositions comprise the AA2024 alloy as the matrix phase and the reinforcing phase consists of silicon carbide, silicon nitride and graphite particulates. The ranking analysis orders are consistent with subjective analysis.

Structural Performance of Additively Manufactured Composite Lattice Structures: Strain Rate, Cell Geometry, and Weight Ratio Effects

The proliferation of ordered cellular structures in industrial and technological applications is justified by their superior mechanical performance, including tunable energy absorption strategies and potential multifunctionality. This research evaluates the mechanical response of composite lattice structures fabricated using vat photopolymerization additive manufacturing process and printable particulate composite materials. Several generations of modified printable resins are prepared by hybridizing flexible resin with varying glass microballoons reinforcement weight percentages. Multifaceted characterization regiments highlight the process–property–performance interrelationship by submitting printed composite structures to quasi‐static and impact‐loading scenarios combined with digital stills and high‐speed photography, respectively. Image analyses of optical and scanning electron micrographs quantify the dimensional accuracy of the composite lattice structures with cylindrical and hexagonal cellular geometries. The mechanical characterization uncovers the effect of cell geometry and reinforcement on the global structural behavior, eliciting differences in load‐bearing capacity, local strain developments, and structural densification. Exploratory digital image correlation supports the global structural deformations, revealing their relationship with the developed local strain state within the unit cells. The outcomes of this research elucidate the effect of strain rate, unit cell geometry, and reinforcing ratios on the structural performance of composite lattice structures at the macro‐ and microstructure levels.

Predicting effective thermal conductivity of fibrous and particulate composite materials using convolutional neural network

Thermal insulation composite materials, such as aerogels and cellular foams, typically exhibit great performance such as low thermal conductivity and complex microstructures at the nanometer or micrometer scale. However, traditional micromechanics-based methods like homogenization and finite element analysis may not accurately predict their effective thermal conductivity due to a limited understanding of microscopic heat transfer mechanisms and the vast amounts of multi-scale microstructural data. In this paper, the effective thermal conductivity of composite materials is predicted using a convolutional neural network (CNN). To model the microstructure of the composites, digital images are generated using the Quartet Structure Generation Set (QSGS) method and the Random Generation-Growth Method (RGGM). The Lattice Boltzmann Method (LBM) is then used to predict the effective thermal conductivities. The CNN model is trained and validated using the microstructural images as input data and the conductivities predicted by LBM as output data. Subsequently, Parametric studies are conducted to investigate the material characteristics, including volume fraction and microstructural anisotropy. Additionally, the CNN predictions are compared with the results of experiments, analytical models, and computational methods. Finally, the proposed CNN model is utilized to predict the thermal conductivity of materials with novel microstructures that are not included in the training set. By capturing the scattering characteristics of heterogeneous materials through artificial intelligence, the CNN model predicts thermal conductivity more efficiently than traditional methods. This highlights the potential of machine learning to advance materials science and accelerate development of materials with desired properties.

Statistical characterization of spatial and size distributions of particles in composite materials used in the manufacturing of biomedical instruments

Aluminium is extensively used in many manufacturing processes because of its intrinsic properties like soft, ductile, high electrical conductivity and highly corrosion resistant. Unfortunately, pure aluminium cannot give a required tensile strength, whereas by adding some other materials like particles of silicon carbide can give a proper strength and converted into a composite with adequate properties which is most suitable in the manufacturing of some specific biomedical instruments. It is well known that size and spatial distributions of particles are both influential in determining the mechanical properties of composite materials and, therefore, statistical characterization of these distributions is of prime importance if we wish to control the quality of the manufacturing processes for these materials. Many researchers have considered quantitative analysis of these features separately, but here we investigate the relationship between size and spatial distribution of the particles over an aluminium matrix. We have considered the actual sizes simply as ‘large’ or ‘small’ and, consequently, the characterization of the particle distribution patterns in the aluminium matrix can be carried out using statistical methods based on the theory of bivariate spatial point processes. We have applied this statistical approach to a sample of an aluminium alloy reinforced with silicon carbide particles. It is shown that the methods provide a complete characterization on the spatial interaction between small and large silicon carbide particles and it can be successfully used in a quality control step for the production of particulate composite materials used in the manufacturing of biomedical instruments.

Dynamic modeling and analysis of flexible micro-porous piezoelectric sensors applicable in soft robotics

With recent advances in system integration technologies, numerous efforts have been made to develop soft piezoelectric sensors for various engineering and healthcare applications. Using flexible and sensitive materials is crucial for designing soft sensors in order to maximize their efficiency and integrability. Micro-porous PU-PZT composite is a recently designed piezoelectric particulate composite material with an improved flexibility and piezoelectric voltage coefficient over common piezoelectric ceramics that makes it a promising candidate for application in soft sensors. In this study, we investigate the dynamic response and sensitivity of the micro-porous PU-PZT composite for applications in soft sensors in both 33 and 31 modes using energy methods. By using the effective field method, the micro-porous PU-PZT composite material properties were extracted and optimized based on the partially experimentally measured properties in order to get a complete picture of the properties of the material. In addition, the effects of changing the sensor geometry by varying the thickness and adding an extra layer between the piezoelectric layers are studied. Finally, a large area sensor based on micro-porous PU-PZT composite is simulated in finite element software, and the effect of several parameters on sensor’s performance is investigated. Dynamic analysis of the sensor shows high sensitivity in both 31 and 33 modes which is a significant improvement compared to the commonly used bulk piezoelectric ceramics. This work has demonstrated that due to the high output voltage and structural flexibility of the micro-porous PU-PZT composite, a flexible large-area sensor would be a suitable choice for artificial skins and smart gloves.

An Insight into Mechanical and Metallurgical Behavior of Hybrid Reinforced Aluminum Metal Matrix Composite

Hybrid reinforced particulate aluminum matrix composite materials (HAMCs) are a breakthrough kind of material made by combining two or more distinct reinforcing components in the aluminum (Al) matrix. Composites with many reinforcing phases offer a superior overall mix of characteristics than composites with only one. This article’s wide literature review of metal matrix composite (MMC) especially for aluminum matrix composites (AMC) was carried out. Discussions of various widely adopted synthesis methods such as stir casting and powder metallurgy have been presented. The effect of various reinforcement ceramic particles such as silicon carbide (SiC), aluminum oxide (Al2O3), graphite (Gr) on the mechanical and metallurgical properties of MMC has been reviewed. The summary of various characterizations such as X-ray diffraction (X-RD), and optical microscopy (OM) including testing such as hardness, tensile, compressive, and tribological behavior has been discussed in detail to demonstrate a full grasp of the many features of HAMCs, such as manufacturing, physicomechanical properties, wear, and corrosion characteristics. Future developments and potentially useful materials as alternative reinforcements are discussed at the end of the review.

Multi-scale dispersive gradient elasticity model with rotation for the particulate composite

Research on the characteristics of composites material has received enormous interest in recent years. The multi-scale nature of composite material leads to employing advanced techniques. Moreover, the presence of a wave with the high-frequency source adds complexity to the analysis. In this paper, a novel multi-scale elasticity model was developed to predict the wave dispersion property of particulate composites. The methodology was based on the simultaneous participation of translational and rotational degrees of freedom in motion equations. The method scheme of gaining motion equations was accomplished by using Taylor's expansion as a continualization method. The framework of the motion equations and restrictions determined the layout of the lattice. The obtained lattice was a network of polydispersive particles in three-dimensional as a unit cell. The model proposed the inertia coefficient and dimension coefficient, which reflected the heterogeneity on the particulate composite. The theoretical results were then validated with experimental results. For this aim, concrete and mortar with the various classes of heterogeneity were nominated as a sample of particulate composite material. The nominated composites were prepared with different mixture schemes from geopolymer paste. The wave dispersion behavior of geopolymer specimens was established by measuring phase velocity in various frequencies. In addition to geopolymer products, experimental results from independent literature were scrutinized for concrete and mortar with cement binder. In the end, the capability of the presented parameters was indicated for realizing the state of concrete curing and features of the binder.

Adaptive stochastic morphology simulation and mesh generation of high-quality 3D particulate composite microstructures with complex surface texture

Particulate composite materials have a broad range of potential applications in engineering and other disciplines. Accurate modeling of their microstructures and fast generation of the finite element meshes play a vital role in investigating many micromechanical phenomena and improving understanding of the underlying failure mechanisms. Due to the exceedingly intricate multiscale internal structures that they possess, the modeling and meshing of their microstructures still remain difficult in general. In this work, we present a computational framework and methodology for the representation, simulation, and mesh generation of 3D stochastic microstructures of particulate composites. Towards this goal, we propose a multi-level multiscale scheme that allows for capturing the multiscale structures of particulate composite materials at both the coarse and fine scales. A briging scale approach based on heat kernel smoothing is also presented to seamlessly link the coarse and fine scales. In addition to the microstructural modeling of particulate composite materials, we also develop an adaptive curvature-based surface and volume mesh generation algorithm for particulate composite microstructures with complex surface texture. Following the implementation of the morphology and mesh generation algorithm, a series of numerical examples are presented to demonstrate the capability and potential of the proposed method.

Publisher's Note: “The thermomechanics of particulate composite mock energetic materials in response to high-frequency (1–100 kHz) excitation” [J. Appl. Phys. 131, 045103 (2022)

Publisher's Note: “The thermomechanics of particulate composite mock energetic materials in response to high-frequency (1–100 kHz) excitation” [J. Appl. Phys. 131, 045103 (2022)

The thermomechanics of particulate composite mock energetic materials in response to high-frequency (1–100 kHz) excitation

This work seeks to explore the macroscale, thermomechanical response of polymer-bonded composite energetic materials in their inert form to high-frequency mechanical excitation in the range of 1–100 kHz. Cylindrical samples were fabricated according to a mock PBXN-109 formulation, consisting of hydroxyl-terminated polybutadiene, mock RDX material (sucrose), and varying ratios of spherical aluminum powder. Experiments were performed utilizing laser Doppler vibrometry and infrared thermography in order to analyze the thermal and mechanical response of the samples when mechanically excited using a piezoelectric shaker. Thermal analysis of the samples revealed temperature rises on the order of 4°C/min, with several samples reaching 100°C within a 15 min experiment. Generally, formulations containing a higher weight percentage of aluminum additive content tended to exhibit greater temperature increases than those with pure sucrose embedded in the binder. The investigation presented herein serves as an advancement toward the complete characterization of these composite materials in this frequency range.

Fabrication and characterization of micro alumina zirconia particulate filled dental restorative composite materials

AbstractComposite resin is quickly becoming the most extensively utilized material in the field of cosmetic and restorative dentistry. It eliminates the problem of poisonous nature caused by the amalgam's mercury content. This research article is based on the outcome of adding micro‐alumina and zirconia particulates filled dental composites on the physical properties, mechanical properties, and wear analysis. Three dental composites named AZDC2, AZDC4, and AZDC6, were prepared. Alumina microparticles were constant (6 wt%) while zirconia microparticles were varying (2, 4, and 6 wt%). Two‐body wear was performed on pin‐on‐disc friction & wear test rig under dry sliding for all three dental composites under different normal load (20, 40, and 60 N) and sliding speed (10, 15, and 20 mm/s). The experiments were designed as per L9 Taguchi orthogonal array. The least volumetric wear was found in AZDC6 dental composite under a sliding velocity of 15 mm/s and a normal load of 20 N. From the ANOVA analysis, normal load and filler content were the significant factors affecting the volumetric wear with a significance level 5%, respectively. Dental composite AZDC6 exhibited maximum compressive strength (451.49 MPa). Micro hybrid particulates‐filled dental composites improve physical and mechanical characterization.

The investigation of the effect of nano particles on dry sliding wear and corrosion behavior of Al-Mg/Al2O3 composites

Aluminum matrix composites were extensively used as structural material as it possesses good surface properties such as wear and corrosion resistance. The practical importance of nano particles in composite materials has triggered widespread attention towards the enhancement of its properties. In this study, Al-Mg/Al2O3 (0–8 wt%) metal matrix nano composites fabricated by two step stir casting route was investigated to comprehend its wear and corrosion behaviour. The Pin-on-Disc dry sliding wear test was performed on Al-Mg/Al2O3 (0–8 wt%) by adopting Design of Experiments under the action of different contact loads and sliding distance following the ASTM G99 Standard. The experimental results conveyed that specific wear rate decreases with increase in sliding distance. Statistical analysis was performed by Taguchi’s Technique and Analysis of Variance (ANOVA) to determine the most dominating factor that influences specific wear rate for the optimum weight percentage of reinforcement. Analysis revealed adequacy with the constructed model in predicting the wear behavior of composite and unreinforced Al-Mg alloy. The corrosion behaviour of the base alloy and composites was analysed by static immersion and electrochemical assessments, by immersing prepared specimens in aqueous sodium chloride (3.5%) solution. The dependance of corrosion rate of the composites with the weight percentage of Al2O3, exposure duration and temperature of the corrosive medium was studied in detail. Corrosion test results exhibit that corrosion rate decreases with increase in weight percentage of Al2O3 particles and exposure duration, whereas it follows reverse trend with increase in corrosion medium temperature.

On the magnetoelectric performance of multiferroic particulate composite materials

In the current work, quantitative analysis of magnetoelectric particulate composite material system explicated the main mechanisms responsible for the below-optimal performance of this class of materials. We considered compliant particulate composite materials, with constituents relevant to technological and scientific interest, leading to 0–3 Terfenol-D/PVDF–TrFE composite samples. To this objective, thick Terfenol-D/PVDF–TrFE films (10–15 µm) were fabricated and analyzed for chemical, mechanical, and magnetic properties to demonstrate their suitability for energy applications in harsh environmental conditions. The vigorous experimental characterization of the composite exemplified the multifunctional properties, quantifying the interrelationship between the composition and performance. We observed that the addition of magnetic particles to the electroactive copolymer matrix resulted in improvement in the mechanical and electrical properties since the particles acted as pinning sites, hindering the deformation of the chains and enhancing polarization. The effective modulus model was amended to account for the crystallization-induced change in material stiffness. We also measured and computed the magnetic particles motion to explicate the detrimental effect of mobility and migration on the overall magnetoelectric coupling performance of the composite. Thereby, we derived an analytical model based on the magnetic force due to the co-presence of alternating and constant magnetic fields, and the viscous drag force due to the viscoelastic properties of the electroactive copolymer matrix. We demonstrated that the mobility of the particles plays a crucial role in the short and long term performance of magnetoelectric coupling in multiferroic particulate composites, uncovering the underpinnings of the dichotomy in performance between experimentally measured and analytically predicted coupling coefficients, thus allowing for the proposal of new approaches to realize the scientific potential of magnetoelectric particulate composites in energy applications.

A Review of Recent Research on Rare Earth Particulate Composite Materials and Structures with Their Applications

A Review of Recent Research on Rare Earth Particulate Composite Materials and Structures with Their Applications

A supervised machine learning approach for accelerating the design of particulate composites: Application to thermal conductivity

A supervised machine learning (ML) based computational methodology for the design of particulate multifunctional composite materials with desired thermal conductivity (TC) is presented. The design variables are physical descriptors of the material microstructure that directly link microstructure to the material’s properties. A sufficiently large and uniformly sampled database was generated based on the Sobol sequence. Microstructures were realized using an efficient dense packing algorithm, and the TCs were obtained using our previously developed Fast Fourier Transform (FFT) homogenization method. Our optimized ML method is trained over the generated database and establishes the complex relationship between the structure and properties. Finally, the application of the trained ML model in the inverse design of a new class of composite materials, liquid metal (LM) elastomer, with desired TC is discussed. The results show that the surrogate model is accurate in predicting the microstructure behavior with respect to high-fidelity FFT simulations, and inverse design is robust in finding microstructure parameters according to case studies.

Effective modulus of Si electrodes considering Li concentration, volume expansion, pore, and Poisson’s ratio of Li-ion batteries

We propose a novel mathematical model for predicting the effective modulus of Si electrodes in Li-ion batteries by considering the large volume expansion of Si during lithiation and porosity variation, as well as the influence of change in Li-ion concentration and Poisson’s ratio, assuming the Si electrode as a particulate composite material. Though previous studies considered the Li-ion concentration and Poisson’s ratio in their models, they rarely considered the porosity and a large change in the volume of silicon, which degrade the performance of Li-ion batteries with Si electrodes. The proposed model is formulated on the basis of a three-phase particulate composite material composed of silicon particles, pores, and binders. Through parametric studies, it is found that the Li-ion concentration in silicon increases nonlinearly and the Poisson’s ratio decreases during charging, regardless of the structure (crystalline or amorphous) of the silicon particles. The effective modulus of the three-phase particulate composite electrode decreases during charging as a result of the changes in the Li-ion concentration, Si and binder volumes, pores, and Poisson’s ratio. The accuracy of the developed model is validated by comparing the predicted moduli of this model with other 3 experimental data and 2 model predictions.

Generalized modeling of the effective thermal conductivity of particulate composites

Accurate and computationally efficient predictive models for the effective thermal conductivity of composites are needed to accelerate the design of new materials with improved properties and behavior. The predictive capabilities of previously developed models for particulate composites (PC) apply to limited ranges of component properties and proportions. Furthermore, existing material models that account for particle contiguity and filler-matrix thermal contact resistance fail to distinguish between those two effects. In this work, two novel and complementary predictive models for the effective thermal conductivity of two-phase isotropic particulate composites are derived: (i) a simple yet efficient analytical model for non-contiguous filler particles, and (ii) a generalized semi-analytical model accounting for both filler particle contiguity and thermal resistance at the filler-matrix interface. The latter model is powered by a thermal conduction grid solver algorithm that allows the incorporation of an unlimited number of elements and components to match increasingly complex particulate composite material configurations and behaviors. The two models proposed in this work can match previously published experimental data fairly well. The grid model is further leveraged to relate the effective thermal conductivity to filler particle size and size distribution. It is found that the formation of filler conduction chains is favored by well-graded particle size distributions.