Extrapolative prediction of the hot strength of austenitic steels with a combined constitutive and ANN model

An integrated phenomenological and artificial neural network (IPANN) model developed previously by Hodgson et al. [P.D. Hodgson, L.X. Kong, C.H.J. Davies, J. Mater. Process. Technol. 87 (19991 132-139] significantly improves the accuracy of the prediction of the hot strength of a commercial 304 stainless steel in comparison with either the phenomenological or the ANN model because of the integration of information developed from a phenomenological constitutive model. In the present work, the Estrin-Mecking constitutive model EY. Estrin, H, Mecking, Acta Metall. 32 (1984) 57-70] was combined with the IPANN model to predict extrapolatively the hot strength of a plain-carbon austenitic steel with a carbon content of 0.79 wt.%, deformed at temperatures from 900 to 1100 degrees C and at strain rates between of 1 and 30 s(-1). The ANN model was able to predict the hot strength over a wider range of deformation conditions using the experimental data and the data from the physical model as ANN training data set. Although, the prediction is not as accurate as if a complete experimental data set had been available, the technique does provide an accurate approach to predict extrapolatively the hot strength of steels with the ANN model. (C) 2000 Elsevier Science S.A. All rights reserved.