Adaptive Pass Scheduling for Roughness Control in Cold Rolling

Abstract

Modern metal forming poses increasing requirements on the surface properties of metallic strips, as these determine not only appearance but also processability during subsequent processing steps such as deep drawing or painting. In this context, an online scheme for pass scheduling of a rolling mill is presented that optimizes the gap adjustments of both roll stands on a tandem rolling mill in order to independently control the geometric and surface properties of the strip, more precisely its thickness and mean surface roughness. The optimization integrates three process models describing the entire rolling process. All models are adapted online to address process disturbances, such as material property variations or the wear of the roughened work rolls. Model adaption is carried out based on sensor data using Gaussian process regression for model identification. Hereby, the control loop is closed by the online adaptation of the underlying process models. Finally, the adaptive pass scheduling is validated on a tandem rolling mill with a DC04 steel of dimensions 1mm × 8.1mm. The experimental results indicate high tracking performance of the proposed optimization scheme as the outgoing strip thickness is successfully kept constant at a level of (0.920±0.008)mm for a thickness reduction of 8% while controlling the surface roughness between 1.1µm and 2.5µm.