Intelligent prediction of rolling force in hot rolling based on a multi-model and weighted support vector machine

In order to improve the set accuracy of rolling force for a finishing mill in the hot rolling process, high precision prediction of rolling force is very important. In this paper, an approximate value of rolling force is calculated through theoretical formulae. And then, a correction coefficient of rolling force is computed using big field data. Firstly, different product states are classfied by the clustering method. Secondly, the correction coefficient is computed based on a weighted least square support vector machine. Through a combination of these two results, the rolling force value with high precision is predicted. The average relative prediction error of rolling force is 3.2%, which can meet the requirements of field production.