Neural network analysis of strain induced transformation behaviour of retained austenite in TRIP-aided steels

Experimental data published in literature were used to develop a neural network model to predict the strain induced transformation behaviour of retained austenite as a function of 13 input variables including chemical composition of the steel, initial retained austenite content, matrix microstructure and forming conditions. The model was found to make reasonable predictions with respect to established metallurgical principles and other published data.