An online roll temperature and thermal crown prediction method based on finite difference and polynomial regression

Abstract

The application discloses an online roll temperature and thermal crown prediction method based on finite difference and polynomial regression, aiming at the technical pain point that the accuracy of the traditional analytical model is insufficient and numerical simulation cannot meet the online real-time calculation demand, a plurality of groups of working conditions are designed through an orthogonal experiment method, and a finite difference method is used to calculate roll steady-state temperature field data to construct a training data set, based on the data set, a polynomial regression algorithm with elastic network regularization is used to fit an axial temperature distribution general model with the roll temperature field as an input variable, after obtaining the online surface axial temperature distribution of the roll, the general model is substituted into the internal temperature field to restore the internal temperature field and calculate the radial thermal expansion amount at each position, so that compensation information is provided for a shape control system of a rolling mill. The application simplifies complex physical field simulation into lightweight algebraic formula operation, retains the high analytical accuracy of the finite difference method, realizes real-time prediction, and can significantly improve the rolling quality of silicon steel products.

Images