Determination of hot and cold rolling textures of steels: combined Bayesian neural network model

Abstract

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

Keywords:

• Artificial neural network
• Texture prediction
• Anisotropy
• Hot rolling
• Cold rolling
• Steel

The authors acknowledge the financial support from the Spanish Ministerio de Ciencia e Innovación through the Plan Nacional 2009 (grant no. ENE2009 13766-C04-01). The authors are also grateful to Neuromat Ltd for the provision of the neural network software used in the present work.