Neural Network-Based Approaches for Forward and Reverse Mappings of Sodium Silicate-Bonded, Carbon Dioxide Gas Hardened Moulding Sand System

Back-propagation neural network and genetic-neural network were developed to predict mould properties of sodium silicate-bonded, carbon dioxide gas hardened moulding sand system from the input process parameters. The performance of back-propagation neural network was found to be comparable with that of the best statistical regression model in predicting the mould properties, whereas genetic-neural network showed large deviations from the target values. Both the said neural networks had been developed as the reverse mapping tools and the performance of genetic-neural network was found to be marginally better than the other. The performances of above neural networks were seen to be dependent on the nature of error surface.

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