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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 continuous annealing process re-transforms the grain inside the strip into uniform equiaxed grains, and at the same time eliminates work-hardening and residual internal stress, and the microstructure and properties of the strip return to the pre-cold deformation state, which is the key process to improve the mechanical properties of the strip . However, the actual production process, the continuous annealing process complicated mechanism, a variety of external operating parameters of the strip have an impact on the performance, coupled with each other, and the detection of strip hardness have a great time lag, Steel hardness index has brought a big obstacle. Using partial least squares method to construct the relationship between the strip hardness and the average path of the process variable, the strip hardness prediction and process monitoring can be realized in time. The feasibility and validity of the proposed method are proved through the simulation analysis of the actual field data.