Sun Youzhao, Li Jingdong, Sun Yamin, Song Lebao, Yang Quan, Wang Xiaochen
National Engineering Research Center of Flat Rolling Equipment, University of Science and Technology Beijing, Beijing 100083, China.
Institute of Engineering Technology, University of Science and Technology Beijing, Beijing 100083, China.
Sensors (Basel). 2024 Jan 18;24(2):614. doi: 10.3390/s24020614.
Focusing on the problem of strip shape quality control in the finishing process of hot rolling, a shape model based on metal flow and stress release with the application of varying contact rolling parameters is introduced. Combined with digital twin technology, the digital twin framework of the shape model is proposed, which realizes the deep integration between physical time-space and virtual time-space. With the utilization of the historical data, the parameters are optimized iteratively to complete the digital twin of the shape model. According to the schedule, the raw material information is taken as the input to obtain the simulation of the strip shape, which shows a variety of export shape conditions. The prediction absolute error of the crown and flatness are less than 5 μm and 5 I-unit, respectively. The results prove that the proposed shape simulation model with strong prediction performance can be effectively applied to hot rolling production. In addition, the proposed model provides operators with a reference for the parameter settings for actual production and promotes the intelligent application of a shape control strategy.
针对热轧精轧过程中板形质量控制问题,介绍了一种基于金属流动和应力释放并应用可变接触轧制参数的板形模型。结合数字孪生技术,提出了板形模型的数字孪生框架,实现了物理时空与虚拟时空的深度融合。利用历史数据对参数进行迭代优化,完成板形模型的数字孪生。按照生产计划,将原料信息作为输入,得到板形模拟结果,并展示了多种出口板形情况。板凸度和平直度的预测绝对误差分别小于5μm和5I单位。结果表明,所提出的具有强预测性能的板形模拟模型能够有效地应用于热轧生产。此外,该模型为操作人员进行实际生产的参数设置提供了参考,促进了板形控制策略的智能应用。