Effects of Carbon Concentration and Cooling Rate on Continuous Cooling Transformations Predicted by Artificial Neural Network

摘要

Employing 151 continuous cooling transformation (CCT) diagrams, and artifcial 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 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 (v), especially for high carbon steels and v>0.1 deg C/s. C prolongs the incubation period of ferrite formation, but accelerates the overall growth kinetics of the pearlite reaction.