Constrained network model for predicting densification behavior of composite powders

1987 ◽  
Vol 2 (1) ◽  
pp. 59-65 ◽  
Author(s):  
F. F. Lange
Keyword(s):  
Network Model ◽  
Powder Compact ◽  
Volume Fraction ◽  
Random Network ◽  
Simple Relation ◽  
Unit Cell ◽  
Densification Behavior ◽  
Inert Particle ◽  
Composite Powders ◽  
Inert Particles

Inert particles that do not contribute to the densification of a composite powder compact are visualized as located on network sites; the network is defined by the distribution of the particles in the powder matrix. Because the distances between neighboring network sites are not identical, the strain produced by the sintering powder between all inert particle pairs cannot be the same as that for the powder compact without the inert particles. The constrained network model is based on the hypothesis that the densification of the composite will be constrained by the network and will mimic that of the network. The shrinkage of the network, and thus the densification of the composite, is estimated with a periodic network. A distance between the minimum and maximum site pairs within the unit cell defines the distance between site pairs in the random network where the powder between the particles densifies in the same manner as that for the powder without the inert particle. When the particles form a continuous touching network, composite shrinkage and densification is nil. The chosen lattice must also conform to this condition. A simple relation was developed relating the densification behavior of the composite to that of the matrix without the inert particles and the parameter associated with the chosen lattice. By choosing the lattice formed by the tetrakaidecahedron unit cell (volume fraction of particles for a touching network = 0.277), remarkable agreement was achieved for the experimental data concerning the densification behavior of the ZnO/SiC composite system reported by De Jonghe et al. [L. C. De Jonghe, M. N. Rahaman, and C. H. Hsueh, Acta Metall. 34, 1467 (1986)]. The universal nature of this lattice for other composites is discussed with respect to site percolation theory. The application of this concept to powder compacts containing either whiskers or agglomerates is briefly discussed.

Estimation of Effective Thermal and Mechanical Properties of Particulate Thermal Interface Materials (TIMs) by a Random Network Model

2017 ◽  
Author(s):  
Pavan Kumar Vaitheeswaran ◽  
Ganesh Subbarayan
Keyword(s):  
Mechanical Properties ◽  
Network Model ◽  
Thermal Stresses ◽  
Volume Fraction ◽  
Random Network ◽  
Thermal Interface Materials ◽  
Thermal And Mechanical Properties ◽  
Thermal Interface ◽  
Classical Models ◽  
Interface Materials

Particulate thermal interface materials (TIMs) are commonly used to transport heat from chip to heat sink. While high thermal conductance is achieved by large volume loadings of highly conducting particles in a compliant matrix, small volume loadings of stiff particles will ensure reduced thermal stresses in the brittle silicon device. Developing numerical models to estimate effective thermal and mechanical properties of TIM systems would help optimize TIM performance with respect to these conflicting requirements. Classical models, often based on single particle solutions or regular arrangement of particles, are insufficient as real-life TIM systems contain a distriubtion of particles at high volume fractions, where classical models are invalid. In our earlier work, a computationally efficient random network model was developed to estimate the effective thermal conductivity of TIM systems [1,2]. This model is extended in this paper to estimate the effective elastic modulus of TIMs. Realistic microstructures are simulated and analyzed using the proposed method. Factors affecting the modulus (volume fraction and particle size distribution) are also studied.

Microstructure and Mechanical Behaviour of Ultrafine Grained Al-4wt%Cu-(2.5-10) Vol.% SiC Metal Matrix Composites Produced by Powder Compact Forging

2011 ◽  
Vol 275 ◽  
pp. 208-213
Author(s):  
A. Gazawi ◽  
De Liang Zhang ◽  
K.L. Pickering ◽  
Aamir Mukhtar
Keyword(s):  
Metal Matrix ◽  
Powder Compact ◽  
Volume Fraction ◽  
High Energy ◽  
Matrix Composites ◽  
Plastic Yielding ◽  
Composite Powders ◽  
Ultrafine Grained ◽  
Powder Compacts ◽  
Small Plastic Strain

Ultrafine grained Al-4wt%Cu-(2.5-10) vol.% SiC metal matrix composite powders were produced from a mixture of Al, Cu and SiC powders using high energy mechanical milling (HEMM). The composite powders produced were first hot pressed at 300°C with a pressure of 240 MPa to produce cylindrical powder compacts with a relative density in the range of 80-94% which decreased with increasing the SiC volume fraction. Powder compact forging was utilized to consolidate the powder compacts into nearly fully dense forged disks. With increasing the volume fraction of SiC from 2.5% to 10%, the average microhardness of the forged disks increased from 73HV to 162HV. The fracture strength of the forged disks increased from 225 to 412 MPa with increasing the volume fraction of SiC particles from 2.5 to 10%. The Al-4wt%Cu-2.5vol.%SiC forged disk did not show any macroscopic plastic yielding, while the Al-4wt%Cu-(7.5 and 10)vol.% SiC forged disk showed macroscopic plastic yielding with a small plastic strain to fracture (~1%).

scholarly journals Sublinear domination and core–periphery networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marios Papachristou
Keyword(s):  
Network Model ◽  
Power Law ◽  
Real World ◽  
Random Network ◽  
Small World ◽  
The Core ◽  
Entire Network

AbstractIn this paper we devise a generative random network model with core–periphery properties whose core nodes act as sublinear dominators, that is, if the network has n nodes, the core has size o(n) and dominates the entire network. We show that instances generated by this model exhibit power law degree distributions, and incorporates small-world phenomena. We also fit our model in a variety of real-world networks.

The Structure of Ion-Implanted Amorphous Silicon

1998 ◽  
Vol 540 ◽  
Author(s):  
J. M. Gibson ◽  
J-Y. Cheng ◽  
P. Voyles ◽  
M.M.J. TREACY ◽  
D.C. Jacobson
Keyword(s):  
Amorphous Silicon ◽  
Network Model ◽  
Random Network ◽  
Amorphous Semiconductors ◽  
Range Order ◽  
Medium Range Order ◽  
Medium Range ◽  
Large Heat ◽  
Continuous Random Network ◽  
Ion Implanted

AbstractUsing fluctuation microscopy, we show that ion-implanted amorphous silicon has more medium-range order than is expected from the continuous random network model. From our previous work on evaporated and sputtered amorphous silicon, we conclude that the structure is paracrystalline, i.e. it possesses crystalline-like order which decays with distance from any point. The observation might pose an explanation for the large heat of relaxation that is evolved by ion-implanted amorphous semiconductors.

The Influence of Multiple Inclusions on the Cauchy Stress of a Spherical Particle-Reinforced Composite Under Uniaxial Loading

2014 ◽  
Author(s):  
Ke Niu ◽  
Armin Abedini ◽  
Zengtao Chen
Keyword(s):  
Spherical Particle ◽  
Single Particle ◽  
Volume Fraction ◽  
Unit Cell ◽  
Spherical Particles ◽  
Uniaxial Tensile ◽  
Cauchy Stress ◽  
Particle Volume ◽  
Volume Fractions ◽  
Unit Cells

This paper investigates the influence of multiple inclusions on the Cauchy stress of a spherical particle-reinforced metal matrix composite (MMC) under uniaxial tensile loading condition. The approach of three-dimensional cubic multi-particle unit cell is used to investigate the 15 non-overlapping identical spherical particles which are randomly distributed in the unit cell. The coordinates of the center of each particle are calculated by using the Random Sequential Adsorption algorithm (RSA) to ensure its periodicity. The models with reinforcement volume fractions of 10%, 15%, 20% and 25% are evaluated by using the finite element method. The behaviour of Cauchy stress for each model is analyzed at a far-field strain of 5%. For each reinforcement volume fraction, four models with different particle spatial distributions are evaluated and averaged to achieve a more accurate result. At the same time, single-particle unit cell and analytical model were developed. The stress-strain curves of multi-particle unit cells are compared with single-particle unit cells and the tangent homogenization model coupled with the Mori-Tanaka method. Only little scatters were found between unit cells with the same particle volume fractions. Multi-particle unit cells predict higher response than single particle unit cells. As the volume fraction of reinforcements increases, the Cauchy stress of MMCs increases.

Evaluation of effective thermal conductivity of unsaturated granular materials using random network model

Geothermics ◽  
2017 ◽  
Vol 67 ◽  
pp. 76-85 ◽  
Author(s):  
Chulho Lee ◽  
Li Zhuang ◽  
Dongseop Lee ◽  
Seokjae Lee ◽  
In-Mo Lee ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Effective Thermal Conductivity ◽  
Granular Materials ◽  
Network Model ◽  
Random Network

scholarly journals Factor or Network Model? Predictions From Neural Networks

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Alexander P. Christensen ◽  
Keyword(s):  
Neural Networks ◽  
Network Model ◽  
Latent Variable ◽  
Random Network ◽  
Network Models ◽  
Small World ◽  
Small World Network ◽  
Monte Carlo Simulation Study ◽  
Psychological Phenomena ◽  
The Neural Networks

The nature of associations between variables is important for constructing theory about psychological phenomena. In the last decade, this topic has received renewed interest with the introduction of psychometric network models. In psychology, network models are often contrasted with latent variable (e.g., factor) models. Recent research has shown that differences between the two tend to be more substantive than statistical. One recently developed algorithm called the Loadings Comparison Test (LCT) was developed to predict whether data were generated from a factor or small-world network model. A significant limitation of the current LCT implementation is that it's based on heuristics that were derived from descriptive statistics. In the present study, we used artificial neural networks to replace these heuristics and develop a more robust and generalizable algorithm. We performed a Monte Carlo simulation study that compared neural networks to the original LCT algorithm as well as logistic regression models that were trained on the same data. We found that the neural networks performed as well as or better than both methods for predicting whether data were generated from a factor, small-world network, or random network model. Although the neural networks were trained on small-world networks, we show that they can reliably predict the data-generating model of random networks, demonstrating generalizability beyond the trained data. We echo the call for more formal theories about the relations between variables and discuss the role of the LCT in this process.

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