INVESTIGATION OF THE MAINTENANCE ORGANISATION FOR HOT ROLLING MILLS

Incremental Support Vector Regression for Steering Hot Rolling Mills

Machine learning concepts, such as the support vector machine (SVM), have been theoretically discussed in great detail, but in industrial applications still rather old-fashioned solutions are preferred. To show that learning concepts improve industrial planning, scheduling, and steering processes, a cooperation with a steel manufacturer was initiated to build a prototype pass schedule calculato...

Research on Crown & Flatness Allocation Strategy of Hot Rolling Mills

In order to present the reasonable crown of hot rolling mills, the bend force and shift position should be pre-computed in a reasonable manner, while maintaining a good profile. How to search the optimal crown trajectory in ratio crown range is the key problem. Crown and flatness allocation strategy is researched based on dynamic programming method which has been improved and optimized by the a...

A Survey of the Looper-tension Control Technology in Hot Rolling Mills

Looper and tension control is important in hot strip mills because it affects the strip quality as well as strip threading. Moreover, the most difficult challange in the controller design and control performance comes from the interaction between looper angle and strip tension. Disturbances from several sources also cause a deterioration in control performance, and thus they should be rejected ...

Rolling conditions in hot strip mills and their influence on the performance of work rolls

The mechanical rolling conditions in hot strip mills are precisely defined by various variables, which are taken directly froth the rolling schedule (separation force, torque. speed. strip thickness) or calculated from figures of the rolling schedule and dimensions of the mill (strip reduction, roll diameter etc.). These variables allow to describe the mechanical rolling conditions of all passe...

GE Energy Mass Flow Control for Intercritical Rolling in Hot Mills