Prediction of mechanical properties in spheroidal cast iron by neural networks

An artificial neural network-based system is proposed to predict mechanical properties in spheroidal cast iron. Several castings of various compositions and modules were produced, starting from different inoculation temperatures and with different cooling times. The mechanical properties were then evaluated by means of tension tests. Process parameters and mechanical properties were then used as a training set for an artificial neural network. Different neural structures were tested, from the simple perceptron up to the multilayer perceptron with two hidden layers, and evaluated by means of a validation set. The results have shown excellent predictive capability of the neural networks as regards maximum tensile strength, when the variation range of strength does not exceed 100 MPa.