Prediction of the continuous cooling transformation diagram of some selected steels using artificial neural networks

Abstract

Continuous cooling transformation (CCT) diagrams play an important role in the description of the transformation behaviour of steels. The experimental determination of a CCT diagram is a very time consuming and expensive task. It would therefore be very attractive to be able to predict CCT diagrams from the chemical composition of the steel and its austenitising temperature. In this article the use of artificial neural networks for the prediction of the transformation start and finish lines in CCT diagrams is described. The data were selected from a single source: The vanadium steels, atlas of continuous cooling transformation diagrams [4].Three neural networks with different numbers of hidden nodes (5‐10‐15) were trained. The number of hidden nodes did not significantly influence the accuracy in the prediction. The network with the least number of hidden nodes (5) was therefore chosen for the evaluation of the performance of the neural networks. This neural network was able to predict the general trends in the CCT diagrams quite well. The relative standard deviation in the prediction of start and end temperatures of each transformation depended on the cooling rate. For the high and low cooling rates it was ∼ 40°C, for the intermediate it rose to 90°C for the ferrite start formation and to 75°C for the other diffusional transformations (pearlite and bainite).The accuracy of the predicted CCT diagram was primarily restricted by the modest quality of the input data used to train the neural network.