Predicting the martensitic transformation start temperature using back-propagation artificial neural networks

The back-propagation artificial neural network was established using data collected from domestic and foreign literatures and the M-s temperatures of some steels were predicted by using the network and compared with those acquired from other methods. Results indicate that the M-s temperatures can be predicted more accurately using artificial neural networks. Moreover, the influence of alloying elements on M-s temperatures was analysed quantitatively using artificial neural networks. The results show that there exists nonlinear relationship between contents of alloying elements in steels and their M-s temperature which is related to the interaction among the alloying elements.