Dynamic compensation of the threading speed drop in rolling processes: Bayesian optimization of the roughing and finishing mill

This paper introduces an advanced approach for dynamic speed drop compensation during threading in rolling processes. The approach combines a data-driven machine learning procedure with a recently presented flatness-based feedforward control to robustly compensate for the speed drop. The feedforward control design accelerates both the rolls and the drivetrain, ensuring that the acceleration torque matches the rolling torque during threading, while maintaining the roll at the desired target speed. Ideally, this prevents the speed drop and enhances the quality and stability of the rolling process. The dynamic speed drop compensation approach is extended in this paper to optimize all stands of a rolling mill, finishing mill as well as roughing mill. To achieve this, the flatness-based feedforward trajectories are adapted to account for uncertainties in the threading event. Moreover, a cost function dependent on optimization parameters is established to optimize the dynamic speed drop compensation. This optimization is carried out using Bayesian Optimization with a Gaussian Process as surrogate model. Both the feedforward control and the Bayesian Optimization run in real-time on an industrial Programmable Logic Controller (PLC). Extensive experimental validation on a hot strip finishing mill, including both the roughing and finishing mill, demonstrates the superior performance of this approach across various key performance indicators in comparison to standard compensation methods. • Extension of method for speed drop compensation during threading in rolling processes. • Real-time Bayesian Optimization of differential flatness-based feedforward trajectories. • Demonstration of superior performance through extensive testing on hot strip finishing mills and roughing mill.