Artificial Neural Networks Based Approaches for the Prediction of Mean Flow Stress in Hot Rolling of Steel

The problem of the estimation of mean flow stress within a hot rolling mill plant for flat steel products is faced, as the correct estimation of this measure can improve the quality of the final product. Various approaches, from standard empirical methods to advanced architectures based on neural networks, have been tested on industrial data. The results of these tests put into evidence the limit of empirical techniques and the big advantages deriving from the application of neural networks, which are able to efficiently combine process knowledge and data driven models tuning. The best performing approaches reduce the estimation error to one third with respect to standard techniques.