Local strain fields and global plastic response of continuous fiber reinforced metal-matrix composites under transverse loading

Werwer, M.; Cornec, A.; Schwalbe, K.‐H.

doi:10.1016/s0927-0256(98)00037-8

Cited by 14 publications

(2 citation statements)

References 12 publications

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“…On the other hand, several random packings have been tried. While these tend to give a more plausible response curve shape , in absolute terms they are inaccurate because they do not get the fiber density right [WCS98, TGL08, SGP06, SG06, TTL06]. This is an active materials science challenge problem, and unfortunately, the data available from physical experiments are inadequate to validate any stress/strain or failure predictions.…”

Section: Applicationsmentioning

confidence: 99%

Disk Density Tuning of a Maximal Random Packing

Ebeida

Rushdi

Awad

et al. 2016

Computer Graphics Forum

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We introduce an algorithmic framework for tuning the spatial density of disks in a maximal random packing, without changing the sizing function or radii of disks. Starting from any maximal random packing such as a Maximal Poisson-disk Sampling (MPS), we iteratively relocate, inject (add), or eject (remove) disks, using a set of three successively more-aggressive local operations. We may achieve a user-defined density, either more dense or more sparse, almost up to the theoretical structured limits. The tuned samples are conflict-free, retain coverage maximality, and, except in the extremes, retain the blue noise randomness properties of the input. We change the density of the packing one disk at a time, maintaining the minimum disk separation distance and the maximum domain coverage distance required of any maximal packing. These properties are local, and we can handle spatially-varying sizing functions. Using fewer points to satisfy a sizing function improves the efficiency of some applications. We apply the framework to improve the quality of meshes, removing non-obtuse angles; and to more accurately model fiber reinforced polymers for elastic and failure simulations.

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Section: Applicationsmentioning

confidence: 99%

Disk Density Tuning of a Maximal Random Packing

Ebeida

Rushdi

Awad

et al. 2016

Computer Graphics Forum

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“…While this structure produces reasonable results under elastic loading, the results produce significant over predictions of strength and damage resistance. Therefore, a random distribution is necessary to accurately predict crack initiation and propagation [20][21][22][23][24].…”

Section: Micromechanicalmentioning

confidence: 99%

A micro to macro approach to polymer matrix composites damage modeling : final LDRD report.

English

Brown

Briggs

2013

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Capabilities are developed, verified and validated to generate constitutive responses using material and geometric measurements with representative volume elements (RVE). The geometrically accurate RVEs are used for determining elastic properties and damage initiation and propagation analysis. Finite element modeling of the meso-structure over the distribution of characterizing measurements is automated and various boundary conditions are applied. Plain and harness weave composites are investigated. Continuum yarn damage, softening behavior and an elastic-plastic matrix are combined with known materials and geometries in order to estimate the macroscopic response as characterized by a set of orthotropic material parameters. Damage mechanics and coupling effects are investigated and macroscopic material models are demonstrated and discussed. Prediction of the elastic, damage, and failure behavior of woven composites will aid in macroscopic constitutive characterization for modeling and optimizing advanced composite systems. 4 5

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