Prediction of the hot deformation behavior for Aermet100 steel using an artificial neural network

Using the experimental data obtained from hot compression tests in the temperature range 800-1200 °C, strain range 0.05-0.90, and strain rate range 0.01-50 s^-1, an artificial neural network (ANN) model is developed to predict the hot deformation behavior of the ultrahigh strength steel of Aermet100. The inputs of the neural network are strain, strain rate and temperature, whereas flow stress is the output. The developed feed-forward back-propagation ANN model is trained with Levenberg-Marquardt learning algorithm. The performance of the ANN model is evaluated using a wide variety of standard statistical indices. Results show that the ANN model can efficiently and accurately predict hot deformation behavior of Aermet100. Finally the extrapolation ability and noise sensitivity of the ANN model are also investigated. It is found that the extrapolation ability is very high in the proximity of the training domain, and the noise tolerance ability very robust.