1999 Volume 39 Issue 10 Pages 1038-1046
Employing 151 continuous cooling transformation (CCT) diagrams, an artificial neural network (ANN) has been modeled and trained. The CCT diagrams of a class of Fe-xC-0.4Si-0.8Mn-1.0Cr-0.003P-0.002S (x within 0.1 through 0.6) steels are predicted by the model developed. It indicates that an increase in carbon concentration (C%) gives rise to a decrease in ferrite start (Fs), bainite start (BS), and martensite start (MS) temperatures, but the carbon concentration has weak effect on the pearlite end (Pe) temperature. The rate of decrease, ∂Fs/∂C, further depends on the carbon concentration. The carbon dependence predicted by the ANN is consistent with what is predicted by thermodynamic models. The Fs temperature is also affected by the cooling rate (υ), especially for high carbon steels and υ>0.1°C/s. C prolongs the incubation period of ferrite formation, but accelerates the overall growth kinetics of the pearlite reaction. The Fs and Pe temperatures at low cooling rates predicted by the ANN model are in agreement with those predicted by thermodynamic models. The deviations of Pe and Fs from their thermodynamic equilibrium counterparts are nearly independent of the carbon concentration. The minimum undercooling for both ferrite and pearlite reactions is around 50°C. It increases up to 100°C at higher cooling rates. Pre-bainite decomposition of austenite retards bainite formation. Employing the Ms temperature, the critical driving force for heterogeneous athermal nucleation is also estimated and related to the Ms temperature. Ms temperatures predicted by this model prove to be consistent with those predicted by several empirical linear models. It can be concluded that the current ANN model is reliable and effective.