Modelling the effect of carbon content on hot strength of steels using a modified artificial neural network

The hot strength of austenitic steels with the carbon content varying from 0.0037 to 0.79 wt% was modelled using artificial neural networks (ANN). The carbon content has a complex effect on flow strength of austenite. An increase in carbon content reduces the flow stress of the steels at high temperatures and low strain rates, while it increases the flow stress at low temperatures and high strain rates, especially at low strains. In addition, increasing carbon to above 0.4 wt% dramatically reduces the peak strain for the initiation of dynamic recrystallisation at high Zener-Hollomon parameter, Z. Given the complexity of the deformation and recrystallisation behaviours of these steels, no phenomenological or simple empirical models are able to predict the flow stress over the full carbon range. In this work, the back error propagation algorithm of the ANN model with one hidden layer bias was used, with the number if hidden nodes optimised. The data up to a strain of 4 were used to predict the strength in both work hardening and dynamic recrystallisation regimes. The training speed was an important parameter and was optimised by trimming the data set and learning procedures. The effects of the carbon content on flow stress, peak strains and peak stresses observed from the experiment were accurately represented. However, it was found that the training data set also needed to be optimised to accurately predict the hot strength of the steels.