In the routine roll shifting mode, work rolls of the continuous variable crown (CVC) hot strip mill are always in repeated shifting positions, which affects the uniform wear of work rolls. As an available solution to the above problem, a new random shifting mode for CVC work rolls has been developed in this paper. According to the relationship between shifting position and bending force, the new CVC shifting mode shifts work rolls in a random pattern within the limits by randomly changing the bending force, so that the roll shifting is dispersed and the strip shape remains good. The Light Gradient Boosting Machine (LightGBM) algorithm is applied to build the prediction models of CVC shifting to accurately express the relationship between shifting position and bending force. Random search and Bayesian optimization are used to optimize the LightGBM models, respectively. By comparison, LightGBM with Bayesian optimization is recommended to predict roll shifting, which is more accurate and efficient than using random search. The new CVC shifting mode has been implemented by an off-line application in the 1780 mm hot rolling line. The results reveal that the proposed CVC shifting mode can well disperse roll shifting positions and accurately control strip shape.

在常规换辊方式下，连续可变凸度（CVC）热轧带钢轧机的工作辊总是处于反复换辊位置，影响工作辊的均匀磨损。为了解决上述问题，本文开发了一种新的 CVC 工作辊随机换档模式。新的CVC换档方式根据换档位置与弯曲力的关系，通过随机改变折弯力，在限制范围内随机移动工作辊，使轧辊位移分散，保持带钢形状良好。应用Light Gradient Boosting Machine (LightGBM)算法建立CVC换挡预测模型，准确表达换挡位置与弯曲力之间的关系。随机搜索和贝叶斯优化分别用于优化 LightGBM 模型。相比之下，推荐使用贝叶斯优化的 LightGBM 来预测滚动位移，这比使用随机搜索更准确和高效。新的 CVC 换档模式已通过 1780 毫米热轧线的离线应用实现。结果表明，所提出的 CVC 换挡模式可以很好地分散轧辊换挡位置并准确控制带钢形状。