Optimization and Prediction Model of Flatness Actuator Efficiency in Cold Rolling Process Based on Process Data

The accuracy of flatness actuator efficiency (FAE) is a prerequisite to achieve high precision automatic flatness control. This study proposes an accurate acquisition approach of FAE based on the combination of simulation modeling and data-driven modeling for flatness control of cold-rolled strip. First, a 3D rolling feature space model is constructed according to the change interval of rolling process parameters based on rolling conditions. A 3D finite element simulation model is subsequently established to determine the prior value of actuator efficiency at each node in the feature space model. Then, to improve the accuracy of prior value, an online optimization model is developed by means of data-driven mechanism. The sorting algorithm and the central limit theorem are introduced to filter the measured process data and calculate the variable weighting. The processed data are imported into the model to improve the fitting model. Finally, a linear output prediction model based on the trend extrapolation method is proposed to realize the accurate acquisition of FAE under any rolling conditions. The data analysis of the production process shows that the model is adaptive and accurate, which can realize high precision flatness control.

Automated summary

The study proposes an accurate acquisition approach of flatness actuator efficiency (FAE) based on the combination of simulation modeling and data-driven modeling for flatness control of cold-rolled strip. The method involves constructing a 3D rolling feature space model, establishing a 3D finite element simulation model, developing an online optimization model, and proposing a linear output prediction model to achieve precise acquisition of FAE under any rolling conditions. The data analysis of the production process shows that the model is adaptive and accurate, capable of realizing high precision flatness control.