Roll force prediction using hybrid genetic algorithm with semi-supervised support vector regression

Abstract Roll force prediction plays a significant role in rolling schedule and optimization. For a specific steel grade, the roll force can be determined by the aid of several factors: rolling speed, initial thickness, ratio of thickness reduction, the starting temperature of the strip, and the friction coefficient in the contact region. Roll force prediction mathematical models are sometimes rare and inaccurate. This paper presents a new approach to predict roll separating force using semi-supervised support vector regression (SSSVR). The parameters affecting the sensitivity of the SSSVR were optimized using the genetic algorithm to maximize the r-squared accuracy score. The intelligent system is evaluated using two quality metrics: the root mean square error (RMSE) and the mean absolute error calculated between the measured force from the industrial rolling field and the predicted force using the proposed system from one side, and the measured force and the calculated force from another side. Obtained results show the improvement while using the intelligent predictive system. The reduction in RMSE was achieved by the proposed system by 66.9% and 32.1% for oval and round shape passes, respectively in comparison to the conventional calculation method.