SUMMARY
The present study has used an ANN to predict shell molding performances – permeability number and transverse strength- of sand moulds employed in hot metal casting. A back propagation ANN is used. This study utilized actual experimental data in which input conditions – CaCo3%, MnO3%, Dwell time and temperature – had been varied and the corresponding resulting performances were recorded. This data was used to both train and validate the ANN with the eventual objective of identifying optimum input conditions that would deliver desirable moulding performance output – the two targeted shell molding properties. The investigation used MATLAB 7.0 while the ANN’s training and validation steps empirically varied learning rate, momentum rate, the number of hidden layers and the number of hidden neurons in each layer. The trained neural network was observed to be capable of predicting the output within 10% error range of their corresponding observed values.