基于平均轨迹的连续退火过程带钢硬度预测方法

Hardness Prediction for CAPL Based on Process Average Trajectory

连续退火工艺使带钢内部晶粒重新转变为均匀等轴晶粒,同时消除加工硬化和残留内应力,带钢的组织和性能恢复到冷变形前状态,是改善带钢的力学性能的关键过程。但实际生产过程中,连续退火过程机理复杂,各种外部操作参数对带钢的性能都能产生影响,彼此间互相耦合,并且对带钢硬度的检测有很大的时间滞后,这对改善带钢硬度指标带来了很大的障碍。选用偏最小二乘方法构建带钢硬度与过程变量平均轨迹之间的关系,可以及时实现带钢硬度预报和过程监测。通过对现场实际数据的仿真分析证明了所提出方法的可行性和有效性。

The grain of strip steel can be turned to uniform. And the work hardening and inner stress can be eliminated by continuous annealing line. So it is the key for improving steel＇ s mechanical properties. But in a real production process, the mechanism of continu- ous annealing is complicated. The coupling operation parameters could change the characteristics of strip steel, and the detection of steel hardness has a long time lag, which brings grate obstacle to improve steel quality. PLS is used to build the relationship between average trajectory process and hardness. It can guarantee the safety of production process by realizing hardness prediction and process monitoring timely. Simulations in real process shows the feasibility and effectiveness of the proposed method.