Prediction of Mean Flow Stress during Hot Strip Rolling Using Genetic Algorithms

In order to satisfy the demands for high accuracy and efficient rolling, it is necessary to establish favourable mathematical model of roll force calculation, which is one of the most important terms for process control. For this purpose it is necessary to predict the Mean Flow Stress (MFS) with good accuracy, since it is the predominant factor of the roll force model. In this paper this problem is dealt on data coming from a real industrial plant and hot compression tests. Various steels have been tested; these can be divided into 2 principal groups: Niobium/Titanium microalloyed, and plain Carbon-Manganese. Particularly, MFS has been found out by measurements taken in the industrial strip rolling mill converting log data in MFS using the Sims approach. Moreover, in order to evaluate the dispersion of MFS measurements, thermomechanical deformation tests have been conducted by a Gleeble 3800 thermomechanical simulator simulating all the seven passes of the studied finishing stand. The results have been analysed and compared to the predictions of some mathematical models developed in literature and it is shown how inadequate well known literature models are. Alternative models have been then proposed by improving existing formulae by means of genetic algorithms based optimization. The performance of the proposed methods have been compared. Moreover, their prediction abilities have been evaluated using the MFS dispersion data measured experimentally. The satisfactory results obtained by optimized based models put into evidence the advantages of the use of artificial intelligence techniques in the industrial framework.