摘要
为了减小热轧机支撑辊动压油膜轴承油膜厚度变化对带钢出口厚度的影响,提高AGC控制精度,建立了基于RBF神经网络的轧机油膜补偿模型;对某2 500 mm单机架热轧中厚板生产线轧机用空压靠法,得到不同轧辊转速和轧制力条件下的轧辊辊缝值,对模型进行训练,并对模型进行仿真。结果表明:基于RBF神经网络的轧机油膜补偿模型能够在线实时地对热轧中厚板生产线油膜厚度变化进行补偿,对改善中厚板带钢的纵向厚差、提高中厚板带钢成材率具有重要意义。
In order to reduce the impact of change of oil film thickness on strip export thickness in dynamic oil film bearing of backup roller and improve the AGC precision,the model of rolling mill oil film compensate was established based on RBF neural network.Using emptypressure methods to a 2 500 mm single-stand hot strip rolling mill,the roller seam values under different roll speed and rolling force were gotten.The model was trained and simulated.The simulation results show that oil film compensate model based on RBF neural network can be used to online real-time compensate oil film thickness changes of hot plate production line.It is important to improve the longitudinal thickness difference of the strip and to increase yield rate of the plate.
出处
《机床与液压》
北大核心
2013年第15期176-178,共3页
Machine Tool & Hydraulics
基金
河南省科技厅科技攻关项目(122102210170)
周口师范学院青年教师基金项目(2012QNB07)
关键词
油膜厚度补偿
中厚板带钢
RBF神经网络
Oil film thickness compensation
Plate steel strip
RBF neural network
