Fuzzy algorithm for calculating roll speed variation based on roll separating force in hot rolling

Abstract

Thickness control of hot-rolled strips has become an important issue in recent years because of the need for improving the quality of the hot-rolled strip. For this purpose, various thickness control systems such as finishing mill set-up (FSU), automatic gauge control (AGC), and looper control system, have been developed at steel works. Although these systems have greatly improved the quality of the strip thickness, there still exists a small amount of thickness deviation. It is difficult to adequately control by applying conventional thickness control techniques since hot rolling process is a highly nonlinear system in which many process parameters are coupled. In this study, a fuzzy algorithm to calculate the roll speed variations was developed in order to improve the thickness uniformity of hot-rolled strips. Since the strip thickness is mostly affected by the magnitude of roll separating force depending on the roll speed, the strip thickness deviation between the desired and actual thicknesses can be reduced by controlling roll speed. In order to carry out this investigation, slab analysis was carried out to determine the relation between roll separating force and roll speed for various process parameters such as roll speed, reduction ratio, strip entry thickness, and front and back tensions. From the production data, the effective stress-strain rate relations of the materials used in slab analyses were acquired. Based on the analytical results, the relation between roll separating force and roll speed was approximated by a log function. A fuzzy algorithm was developed to determine variations in roll speed according to variations of roll separating force, depending on various ranges of rolling temperature, reduction ratio, front and back tensions, and strip thickness. In addition, simulations to predict roll speed variations for a small amount of thickness deviation were carried out at continuous finishing mills consisting of seven stands and the calculated roll speed variations were found to be reasonable. Thus, the developed fuzzy algorithm might be useful in reducing the thickness deviation in the actual hot rolling mills.