Determination of hot- and cold-rolling texture of steels: A combined Bayesian Neural Network model

The work reported in this paper outlines the use of a combined artificial neural network model capable of fast online prediction of textures in low and extra low carbon steels. We approach the problem by a Bayesian framework neural network model that take into account as input to the model the influence of 23 parameters describing chemical composition, and thermomechanical processes such as austenite and ferrite rolling, coiling, cold working and subsequent annealing involved on the production route of low and extra low carbon steels. The output of the model is in the form of fiber texture data. The predictions of the network provide an excellent match to the experimentally measured data. The results presented in this paper demonstrate that this model can help on optimizing the normal anisotropy (rm) of steel products