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Please use this identifier to cite or link to this item: http://dspace.library.iitb.ac.in/jspui/handle/10054/6397

Title: Artificial neural networks for predicting the macromechanical behaviour of ceramic-matrix composites
Authors: RAO, HS
MUKHERJEE, A
Issue Date: 1996
Publisher: ELSEVIER SCIENCE BV
Citation: COMPUTATIONAL MATERIALS SCIENCE, 5(4), 307-322
Abstract: In ceramic-matrix composites (CMCs) a weak fibre/matrix interface is required to achieve satisfactory toughening so that the composite exhibits damage tolerant characteristics. Due to the presence of such a weak interface, debonding and sliding occur at the interface making the mechanics of the material very complex. As a result, developing analytical models for simulating the macromechanical behaviour of these composites is extremely difficult and necessitates simplifying assumptions compromising accuracy. In the present paper, a novel approach to modelling the macromechanical behaviour of CMCs, using the artificial neural network (ANN) approach has been presented. The ability of neural networks in learning the complex multi-parametric interaction among the various microstructural parameters has been demonstrated with an example of SiC/SiC ceramic composite. An artificial neural network has been used to postulate the macromechanical behaviour of SIC (matrix)/SiC (fibre) composite. The training examples for the network have been generated through an accurate micromechanical finite element analysis that models the interfacial debonding and sliding realistically. The network learning is demonstrated and the network is validated by asking it to predict the behaviour of the composite for new specimens. Various stages in the development of ANN such as the preparation of training set, selection of a network configuration, training of the net and a testing scheme, etc. have been addressed at length in this paper.
URI: http://dx.doi.org/10.1016/0927-0256(95)00002-X
http://dspace.library.iitb.ac.in/xmlui/handle/10054/6397
http://hdl.handle.net/10054/6397
ISSN: 0927-0256
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