针对冷连轧机组中的6辊CVC轧机，利用大型非线性有限元软件MSC.Marc建立仿真模型，对多种轧制条件下轧件的弹塑性变形和辊系的弹性变形进行了耦合计算，得到了6辊CVC冷轧机工作辊弯辊、中间辊弯辊、中间辊横移的板形调控功效离散值。研究了不同轧制工艺参数、轧件参数和轧辊参数对各板形调节机构调控功效的影响规律，并得出相应的板形调控功效系数。以有限元计算结果为样本，利用BP神经网络强大的非线性映射功能，建立了板形调控功效的神经网络计算模型，为板形在线闭环控制模型提供高精度的板形调控功效，解决了有限元计算耗时长，难以满足在线控制要求的问题。
Using nonlinear elastic-plastic finite element method, a 3D FE simulation model of six-high CVC rolling process is developed with the nonlinear FE software MSC.Marc. In the model, the elastic deformation of rolls and the elastic-plastic deformation of workpiece were coupled as a whole. Based on the model, the changes of work roll bending force, intermediate roll bending, and intermediate roll transverse shifting were simulated. The effects of different rolling parameters, strip and rollers parameters on the strip flatness adjusting were investigated and the actuators’ corresponding flatness efficiency was obtained. The simulation results of the actuators efficiencies are served as the sample database of BP neural network which delivers high precision results of actuators efficiency to the automatic flatness control system. The problem that the finite element method is time consuming and difficult to be used to the online flatness control is solved, and the precision of flatness online control is enhanced by this method.
参考文献
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