Optimal Parameter Identification for a Hydrodynamic Roll Gap Model in Hot Strip Rolling

Abstract

When using modern model-based control concepts in a hot strip rolling mill, an accurate prediction of the roll force is crucial for the achieved control accuracy in terms of the strip thickness. Hence, the influence of various process parameters, e.g., lubrication, on the roll force is of central interest. Commonly used roll gap models are based on the slab method and are often not capable of describing these influences. The hydrodynamic roll gap model, however, can capture the influence of varying friction conditions between the strip and the work rolls. Varying friction conditions can be caused, for instance, by lubrication. This paper presents a combination of the hydrodynamic roll gap model with tailored material models for a more accurate prediction of the roll force in a tandem rolling mill. To parametrize the model, an optimization-based identification algorithm is proposed for unknown friction coefficients and material parameters. The identification algorithm is used on measurement data from an industrial hot strip rolling mill. A comparison with a commonly used roll gap model shows that the parametrized hydrodynamic roll gap model predicts the roll force with significantly higher accuracy.