Materialometrical approach of predicting the austenite formation temperatures

Artificial neural network model-one of materialometrical approaches was developed basing on experimental data collected from domestic and foreign literatures to predict the austenite formation temperatures (Ac3 and Ac1) of steels. Scatters diagrams and statistical criteria showed that the prediction performance of artificial neural network is superior to that of Andrews formulae. Moreover, the quantitative effects of alloying elements on Ac3 and Ac1 temperatures were analysed using neural network models, the results showed that there exists nonlinear relationship between contents of alloying elements and the Ac3 and Ac1 temperatures which is mainly related to the interaction among the alloying elements in steels.