Optimal Estimation and Feedforward Control of Strip-Longitudinal Hardness for Thickness Hunting Suppression of Tandem Cold Mill Process

Abstract

Following the recent trend of weight reduction in car industry, producing high quality cold-rolled AHSS (advanced high-strength steel) strip becomes important. Thickness hunting (or fluctuation) problem can be more prominent for this cold-rolled AHSS strip making, which can stem from the non-uniformity of hot-rolled strip and can severely degrade product quality. In this paper, we propose a novel framework to estimate the strip-longitudinal hardness of the TCM (tandem cold mill) process and its feedforward control to substantially reduce the thickness hunting, while fully incorporating the interconnected nature and sensing sparsity of the TCM process. In particular, our estimator consists of the following two complementary loops: 1) fast real-time hardness estimation loop, which optimally fuses the process model and sensing information; and 2) slower constant process-parameter estimation loop via optimization utilizing the nonlinear process model and (stored/measured) sensor data. Efficacy of the proposed estimation and control frameworks are then validated with high-precision TCM process physics simulator.