Three dimensional characterization and modeling of particle reinforced metal matrix composites: part I

Li, M; Ghosh, Somnath; Richmond, O.; Weiland, Hasso; Rouns, T. N.

doi:10.1016/s0921-5093(98)01132-0

Cited by 134 publications

(66 citation statements)

References 27 publications

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“…(15), (17), (18), (19), (24) and (25) based on the decomposed model, that in the absence of particle damage calculated by eq. (20) and that in the absence of particle clustering tendency calculated by eq. (27).…”

Section: )mentioning

confidence: 96%

“…[17][18][19][20] Here, a 2-dimensional local number, LN2D, is introduced to quantitatively evaluate the clustering tendency of the composite, detailed in our previous paper. 21,22) In our previous paper, 23) a simple model was developed to explain the flow behavior of the homogeneous composites having different volume fraction, especially considering the spatial distribution of the damaged particles and gave a good fit to the experimental results.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Flow Behavior in the Heterogeneously Dispersed Al-10 vol%SiC Composites

et al. 2008

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A newly developed model was proposed to predict the flow and strain-hardening behavior of the heterogeneously dispersed Al-10 vol%SiC composites with particle damage in tensile deformation. The frequency of local number of particles was decomposed into several Poisson distributions and the frequency of local number of damaged particles was approximated by one Poisson distribution. Then, the flow stress was obtained by summation of strain-hardening rate for each decomposed distribution and/or relieved stress for the distribution of damaged particles. This model gave a good agreement with the experimental results.

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Section: )mentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Prediction of Flow Behavior in the Heterogeneously Dispersed Al-10 vol%SiC Composites

et al. 2008

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“…Our work exploits the properties of the Delaunay network (the counterpart to Voronoi/Dirichlet tessellation which defines each Voronoi polygon as the region of the system in which a specific particle is found to be the nearest), that is generated using the positions of nanoparticles [11][12][13][14][15][16]9]. Good reviews and alternative methods are also provided by [8,[17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Quantifying nanoparticle dispersion: application of the Delaunay network for objective analysis of sample micrographs

et al. 2011

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Measuring quantitatively the nanoparticle dispersion of a composite material requires more than choosing a particular parameter and determining its correspondence to good and bad dispersion. It additionally requires anticipation of the measure's behaviour towards imperfect experimental data, such as that which can be obtained from a limited number of samples. It should be recognised that different samples from a common parent population can give statistically different responses due to sample variation alone and a measure of the likelihood of this occurring allows a decision on the dispersion to be made. It is also important to factor into the analysis the quality of the data in the micrograph with it: (a) being incomplete because some of the particles present in the micrograph are indistinguishable or go unseen; (b) including additional responses which are false. With the use of our preferred method, this paper investigates the effects on the measured dispersion quality of nanoparticles of the micrograph's magnification settings, the role of the fraction of nanoparticles visible and the number of micrographs used. It is demonstrated that the best choice of magnification, which gives the clearest indication of dispersion type, is dependent on the type of nanoparticle structure present. Furthermore it is found that the measured

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“…Although serial sectioning is a time-consuming method, the pores can be more precisely observed than by X-ray CT observation. Serial sectioning has been applied to a number of investigations as a technique for the detailed threedimensional visualization of microstructures of metallic alloys, [10][11][12][13] particle-reinforced metal-matrix composites, [14][15][16] and the morphology of pores in Mg alloy highpressure die castings. 17,18) The aim of this study is to reveal the precise geometric features of CSP, and to investigate the effect of CSP on the fatigue strength of aluminum alloy die castings.…”

Section: Introductionmentioning

confidence: 99%

Clustered Shrinkage Pores in Ill-Conditioned Aluminum Alloy Die Castings

et al. 2010

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The geometric features of clustered shrinkage pores (CSP) in ill-conditioned aluminum alloy die castings were revealed and their effect on the fatigue strength was discussed. To obtain the geometric features of CSP, an observation using a commercial microfocus X-ray computed tomography (X-ray CT) system was carried out and the position and size of CSP were confirmed. However, the detailed geometry of the CSP could not be clearly observed by X-ray CT, because each pore in the CSP was too small to observe owing to the insufficient resolution of the CT image. We developed a serial sectioning system with a polishing machine and an optical microscope. Observation using the serial sectioning system clearly showed that a CSP consists of many interconnected small pores, which formed an extremely complicated shape. The CSP was thought to grow from relatively large gas pores, which connect to small pores and consequently generate a cavity with a huge volume and a complicated geometry. The complex geometry of CSP resulted in the concentration of stress around CSP, and significantly undermines mechanical properties such as tensile strength and fatigue strength.

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Three dimensional characterization and modeling of particle reinforced metal matrix composites: part I

Cited by 134 publications

References 27 publications

Prediction of Flow Behavior in the Heterogeneously Dispersed Al-10 vol%SiC Composites

Prediction of Flow Behavior in the Heterogeneously Dispersed Al-10 vol%SiC Composites

Quantifying nanoparticle dispersion: application of the Delaunay network for objective analysis of sample micrographs

Clustered Shrinkage Pores in Ill-Conditioned Aluminum Alloy Die Castings

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