Improvement of the prediction accuracy and efficiency of hot strength of austenitic steels with optimised ANN training schemes

The hot strength of austenitic steels of different carbon contents was modelled using an artificial neural network (ANN) model with optimum training data. As training data employed in a traditional neural network model were randomly selected from experimental data, they were not representative and the prediction accuracy and efficiency were therefore significantly affected. In this work, only representatively experimental data were used for training and during the procedure, one tenth of the training data extracted from experiment were used for testing the training model and terminating the modelling. The effects of the carbon content on flow stress, peak strains and peak stresses observed from the experiment for both training and test data were accurately represented with the ANN scheme reported in this work.