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. Dav ies, J. Mater. Process. Technol. 87 (19991 132-139] significantly improves the accuracy of the prediction of the hot strength of a commercial 304 stai nless steel in comparison with either the phenomenological or the ANN model because of the integration of information developed from a phenomenologica l constitutive model. In the present work, the Estrin-Mecking constitutive model EY. Estrin, H, Mecking, Acta Metall. 32 (1984) 57-70] was combined wi th the IPANN model to predict extrapolatively the hot strength of a plain-c arbon austenitic steel with a carbon content of 0.79 wt.%, deformed at temp eratures 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 ran ge of deformation conditions using the experimental data and the data from the physical model as ANN training data set. Although, the prediction is no t as accurate as if a complete experimental data set had been available, th e technique does provide an accurate approach to predict extrapolatively th e hot strength of steels with the ANN model. (C) 2000 Elsevier Science S.A. All rights reserved.