Modelling of input–output relationships in cement bonded moulding sand system using neural networks

Abstract

Cement bonded sand moulds can be used to cast ferrous metals with a good dimensional control. To determine input–output relationships in the cement bonded moulding sand system, both forward and reverse mappings were carried out using feed forward neural networks trained with the help of a back propagation algorithm and a genetic algorithm, separately. In the forward mapping, mould properties, namely compression strength and hardness, were predicted for different combinations of process parameters, such as percentages of cement, of accelerator and of water and testing time. In the reverse mapping, the process parameters were determined as the functions of mould properties. A batch mode of training had been provided to the neural networks with the help of one thousand training data generated artificially using the conventional statistical regression equations derived earlier by the authors. The performances of the developed models were compared among themselves and with those of the statistical regression model, for twenty randomly generated test cases. Neural network based approaches had proved their ability to carry out both the mappings. In forward mapping, the results of the neural network based approaches were found to be comparable with those of conventional regression analysis. Moreover, the genetic algorithm trained neural network was seen to perform better than the back propagation trained neural network for both the forward and reverse mappings.