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IJEMS Vol.19(2) [April 2012] >

Title: Prediction performance of various numerical model training algorithms in solidification process of A356 matrix composites
Authors: Shabani, Mohsen Ostad
Mazahery, Ali
Keywords: Finite element
Artificial intelligence
Metal matrix composite
Issue Date: Apr-2012
Publisher: NISCAIR-CSIR, India
Abstract: This paper reports the microstructural and mechanical properties of casting Al matrix composite such as porosity, hardness and tensile strength. The numerical model and finite element method are applied to simulate the solidification of the composites. The finite element analysis involves a number of steps such as finite-element discretization, imposition of boundary conditions and solution of assembled equations. The mathematical formulation of this solidification problem is given. The neural network predictions are directly compared with the experimentally obtained data to evaluate the learning performance. In this investigation the MAPE is used to evaluate the performance of model. The results show that Levenberg-Marquardt learning algorithm give the best prediction for UTS, hardness and porosity of A356 composite reinforced with B4C particulates.
Page(s): 129-134
CC License:  CC Attribution-Noncommercial-No Derivative Works 2.5 India
ISSN: 0975-1017 (Online); 0971-4588 (Print)
Source:IJEMS Vol.19(2) [April 2012]

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