Simulation and quantitative assessment of homogeneous and inhomogeneous particle distributions in particulate metal matrix composites

Abstract

SummaryReinforcement distributions play an important role in various aspects of the processing and final mechanical behaviour of particulate metal matrix composites (PMMCs). Methods for quantifying spatial distribution in such materials are, however, poorly developed, particularly in relation to the range of particle size, shape and orientation that may be present in any one system. The present work investigates via computer simulations the influences of particle morphology, homogeneity and inhomogeneity on spatial distribution measurements obtained by finite-body tessellation. Distribution inhomogeneity was simulated both by the segregation of particles away from specified regions within a microstructure and by generating point density peaks at random locations within a microstructure. Both isotropic and anisotropic inhomogeneous distributions were considered to simulate distribution patterns in PMMCs before and after mechanical working. It was found that the coefficient of variation of the mean near-neighbour distance (COV(d mean )), derived from particle interfaces using finite-body tessellation, was essentially independent of particle shape, size distribution, orientation and area fraction in homogeneous (random) distributions, but showed great sensitivity to inhomogeneity. Increased values of COV(d mean ) were seen for both forms of inhomogeneous distributions considered here, with little influence of particle morphology. The COV(d mean ) was also seen to be sensitive to anisotropic clustering, the presence of which was identified via nearest-neighbour angles and cell orientations. Although generally formulated for PMMCs, the present results may be generalized to other systems containing low aspect ratio finite bodies of low to moderate area fraction.

Fatigue crack growth in a particulate TiB2-reinforced powder metallurgy iron-based composite

“…It has been found previously that such interface-based finite-body tessellation provides a valuable method of assessing particle distributions in common particulate MMC microstructures, offering significant advantages over Dirichlet tessellation and nearest-neighbor methods. [14,15,16] A variety of parameters relating to particle morphology and spatial distribution may then be derived for each individual particle, for instance, "local" area fraction (ratio of particle area and corresponding tessellation cell area), and mean near-neighbor distance (average of the interface-to-interface separations with all particles that share a cell edge around each individual particle of interest). In particular, the coefficient of variation of mean near-neighbor distance (COV(d mean )), defined as the ratio of the standard deviation and the average of mean near-neighbor distances, has been identified in previous work as a particle-morphologyindependent parameter to quantify homogeneity of particle distributions.…”

“…In particular, the coefficient of variation of mean near-neighbor distance (COV(d mean )), defined as the ratio of the standard deviation and the average of mean near-neighbor distances, has been identified in previous work as a particle-morphologyindependent parameter to quantify homogeneity of particle distributions. [15,16] It has been shown that a COV(d mean ) value of is indicative of a homogeneous (in this context being taken to mean random) particle distribution. [15,16] Standard image analysis and finite-body tessellation measurements of the present particulate TiB 2 reinforced composite showed that the particles had an average diameter of (as equivalent area circles) and average aspect ratio of 1.4.…”

“…[15,16] It has been shown that a COV(d mean ) value of is indicative of a homogeneous (in this context being taken to mean random) particle distribution. [15,16] Standard image analysis and finite-body tessellation measurements of the present particulate TiB 2 reinforced composite showed that the particles had an average diameter of (as equivalent area circles) and average aspect ratio of 1.4.…”

Advanced numerical method for generation of three-dimensional particles and its application in microstructure-based simulation of fatigue behavior

“…The Box-Müller method was also adapted to get a log-normal distribution [32]. Figure 3 gives an example of ellipsoidal generation of inclusions with the localized cluster method [9] and the remeshing method previously described. Some generated REVs were containing more than 300 inclusions gathered in a single cluster.…”

Investigation and characterization of fluidity and microstructure of recycled AL-B4C materials /

“…It is given by the inner envelope of the perpendicular bisectors of the lines joining the given point to the other points. Based on the tessellated cell structure, a variety of parameters relating to spatial distribution may be derived, including "neighboring particle" parameters (defined as cells sharing cell boundaries) and near-neighbor distance (defined as the shortest distance between corresponding particle centroids) [19] . For example, Lloyd [65] studied the particle distribution in two particle reinforce MMCs.…”