带钢冷轧过程辊系径向变形的数据挖掘与预报

Data Mining and Prediction for the Radial Deformation of Rollers in the Steel Strip Cold Rolling Process

在带钢冷轧过程中,辊系径向变形会影响带钢的厚度精度,并且难以在线检测。依据自动厚度控制(Auto gauge control,AGC)系统固有的测试系统,充分利用采集到的出口厚度、压下位移、轧制力、轧制速度等数据,根据厚差公式进行数据挖掘,对辊系的变形进行挖掘计算。为了解决由于测厚仪安装距离造成的厚差信号滞后问题,在挖掘计算前依据轧制速度进行数据重新匹配和整理,在挖掘计算后采用指数平滑法对计算结果进行平滑,并按照趋势外推法进行当前辊系径向变形值的预报。采用平滑系数自适应算法进一步提高预报精度。将辊系径向变形值的挖掘和预报方法在某四辊轧机轧制数据上进行应用,取得比较清晰的变形规律和完整变形过程。为进一步研究轧辊辊系径向变形规律和补差方法,进而提高厚度控制精度奠定了基础。

In the steel strip cold rolling process,the radial deformation of roll system will affect the thickness precision of the steel strip,and it is difficult to be detected on line.In allusion to this problem,a method of data mining is put forward Based on the measuring system of auto gauge control(AGC),and by using the collected data such as the exit thickness,downstroke,rolling force and rolling speed,data mining is carried out on the basis of the thickness difference formula,and the roll deformation is computed.Before the data mining,the data are rematched and arranged to solve the problem of thickness difference signal hysteresis caused by the mounting distance of thickness meter.After the date mining,the exponential smoothing is used to smoothen the computed result,and the trends extrapolation is introduced to predict the current radial deformation value of the roll system.Adaptive smooth coefficient algorithm is adopted to further improve the prediction precision.The data mining and prediction methods for radial deformation of roll system are applied to a 4-high mill,a clear and complete deformation process is obtained,which lays the foundation for further research on the axial deformation rule of roll system and the compensation method,thereby improving the thickness control precision.