Artificial neural network to predict the effect of thermomechanical treatments on bake hardenability of low carbon steels

Abstract

Artificial neural network (ANN) is a powerful tool in optimizing many industrial processes. In the present study, an ANN was developed to model and predict the bake hardenability and final yield stress of low carbon steels. Five parameters affecting the bake hardening were considered as inputs, including the annealing/reheating temperature, cooling rate, initial yield stress, work hardening and carbon level. The network was then trained so that to predict the bake hardening amounts and the final yield stress as outputs. A Multilayer cascade-forward back-propagation network is developed and trained using experimental work. Two low carbon steels, St12 and St14, were investigated. The effects of annealing/reheating temperature (500–1000 °C) and subsequent cooling rate (0.5, 5 and 500 °C/s) on the bake hardenability of steels were modeled by ANN as well. In terms of cooling rate, two different behaviors were observed. The bake hardenability of St12 was increased from 13 ± 2, for reheating to 500 °C, to 88 ± 2 MPa for the reheating temperature of 1000 °C. As for St14, these values were respectively 8 ± 2 and 67 ± 2 MPa. The predicted values are in very good agreement with the measured ones indicating that the developed model is very accurate and has the great ability for predicting the bake hardenability.