%0 Journal Article
%T Use of Artificial Neural Network and Theoretical Modeling to Predict the Effective Elastic Modulus of Composites with Ellipsoidal Inclusions
%A Anupama Upadhyay
%A Ramvir Singh
%J Open Access Library Journal
%V 1
%N 7
%P 1-14
%@ 2333-9721
%D 2014
%I Open Access Library
%R 10.4236/oalib.1100903
%X
In this paper, a possible
applicability of artificial neural networks to predict the elastic modulus of composites
with ellipsoidal inclusions is investigated. Besides it, based on the general micromechanical
unit cell approach, theoretical formula is also developed, for effective elastic
modulus of composites containing randomly dispersed ellipsoidal in homogeneities. Developed theoretical model considers
the ellipsoidal particles to be arranged in a three-dimensional cubic array. The
arrangement has been divided into unit cells, each of which contains an ellipsoid.
Practically in real composite systems neither isostress is there, nor
isostrain, and besides it due to the effect of random packing of the phases,
non-uniform shape of the particles, we are forced to include an empirical
correction factor. We are forced to include an empirical correction factor in place
of volume fraction which provided a modified expression for effective elastic modulus.
Empirical correction factor is correlated in terms of the ratio of elastic moduli
and the volume fractions of the constituents. Numerical simulations has also been
done using artificial neural network and compared with the results of Halpin-Tsai
and Mori-Tanaka models as well as with experimental results
as cited in the literature. Calculation has been done for the samples of Glass fiber/nylon 6 composite (MMW nylon
6/glass fiber), Organically modified montmorillonite (MMT)/High molecular
weight (HMW) nylon 6 nanocomposite ((HE)2M1R1-HMW
nylon 6), Epoxy-alumina composites and MXD6- clay nanocomposite. It is found that
both the theoretical predictions by the proposed model and ANN results are in close
agreement with the experimental results.
%K Inclusion
%K Elastic
%K Composite
%K Isostress
%K Isostrain
%U http://www.oalib.com/paper/3104545