Rolling Force Adaptive Learning of Tandem Cold Rolling Mill Using PSO Algorithm
In tandem cold rolling, rolling force is an important parameter to AGC (Automatic Gauge Control) system. To improve the precision of rolling force prediction, online self-adaption software is developed for a five-stand tandem cold rolling mill. Using exponential smoothing method a rolling force self-adaption algorithm based on measured data is designed. The new rolling force model coefficient is made of the ratio of measured value and new setting value which is recalculated by other measured variables. As the main influencing factor of rolling force, the deformation resistance of strip material is recalculated and fit of parameter using particle swarm optimization algorithm. Through the online self-adaption and self-learning, the model of rolling force has steady performance and high precision, and the rolling efficiency of tandem cold rolling mill is highly improved.
tandem cold rolling rolling force self-adaption particle swarm optimization
Chen Dongning Jiang Wanlu
College of Mechanical Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, China
国际会议
北戴河
英文
536-539
2007-06-06（万方平台首次上网日期，不代表论文的发表时间）