PREDICTION OF THE ALUMINUM SILICON MODIFICATION LEVEL IN THE AlSiCu ALLOYS USING ARTIFICIAL NEURAL NETWORKS
نویسندگان
چکیده
In this paper, two feed forward neural network models have been presented to predict the Silicon Modification Level (SiML) of W319 aluminum alloys using the Thermal Analysis (T.A) parameters as inputs. The developed neural networks are a Multilayer Perceptron (MLP) network and a Radial Basis Function (RBF) network. The neural network models were found to predict the SiML accurately (R=0.99). The accuracy of the Neural Network Models has been compared with the existing ∆T method and a linear multiple regression model. The comparison of the RBF and MLP networks has shown that the RBF requires much lesser training time than MLP.
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