4 research outputs found
Gain Tuning Model of Human Expert for Looper Height Control in Hot Strip Rolling
In hot strip rolling mills, the looper control system is automated. However, the looper's behavior tends to be unstable in threading. Therefore, human expert always intervenes and stabilizes the looper's behavior by tuning PID gains and interposing manipulation variable of looper control system. In this paper, we propose a method based on the recurrent neural network to express PID gains tuning action by human. Furthermore, we propose two methods to update the model by learning. To check the effectiveness of the proposed learning methods, numerical simulation applied to the looper height control is carried out
Methods of Diagnosis and Intervention for Agent of Hot Rolling Operation Support
In the last two decades, it becomes possible to automate operations of various steel plants especially in rolling mills. As the results, stabilization of productivity and improvement of product quality have been attained. On the while, in these years, many skilled engineers and operators who actively promoted economical growth of steel industries will retire due to their age limits. Thus, the inheritance of the high level technology and know-how has becomes a serious problem. To overcome the problem, it is necessary to extract knowledge of the skilled persons and make technical textbook reducing tacit knowledge. In this paper, rules are extracted from the operation data of hot strip rolling applicable to the operation diagnosis and intervention during operation. To attain the object, agent based simulator of hot strip rolling has been developed to prepare various rolling data for extraction of diagnosis and intervention rules in rolling operations. As for the selection of normal and abnormal data, SVM algorithm is tested before rules extraction. Rules are written in Fuzzy logic forms and its parameters are optimized by GA algorithm. These technologies are involved in the operation support agent system of hot strip rolling mills together with RNN for automatic gain tuning of mill controller