# Intelligent Optimization of Process Parameters in Limit Gauge Hot Strip Rolling
## 1 Introduction  
The limit specification of hot strip rolling generally refers to the extremely wide or thin specification of a certain kind of steel in strip production. In actual production, the extremely thin specification is in the majority. In recent years, experts from some iron and steel production enterprises have carried out research on the production technology of extreme specifications based on the production site [1, 2]. Studies have shown that extreme gauge rolling is characterized by: less rolling quantity, lower rolling stability than the conventional product specification, more preparation procedures (hot roll transition material, etc. ), high equipment status requirements (rigidity, looper, mill adjustment), and special process requirements (adjusting load distribution and rolling speed, controlling rough rolling sickle bend, etc.). At the same time, some scholars have studied the control strategy and process system of the rolling process [3, 4], and the research shows that the upper limit of limit specification width is not only related to the design specification of the rolling mill, but also related to the control technology, process system, and equipment working state; in addition to the design capacity of rolling mill unit, the lower limit of limit specification thickness is also closely related to control technology, process system, and equipment working status. Before the production of limit specification in the current workshop, the process personnel generally formulates the production process and equipment status system. After the process specification is given by the process control system, the equipment and process are maintained, modified and confirmed item by item in combination with the current automatic data acquisition signal and equipment routine inspection results, and the production task is executed after meeting the production conditions. The problems in thewhole process are: difficult analysis, complex production system and process, experience dependent rolling stability, unstable product quality, hidden danger of equipment damage, etc. [5]. In recent years, some scholars have put forward some experience and standards for the production of limit specifications of hot strip rolling, but they all put forward specific rolling suggestions for the specified specifications. These process parameters and equipment status have little guiding significance for other steel types and specifications [6; 7]. At the same time, some scholars have analyzed the relevant equipment and technology of limit gauge rolling and believe that the current dimension control is a mature technology, and the key to improvement lies in the adjustment and maintenance method, while the artificial intelligence method is a powerful tool, which can fill the gap between the physical model and the actual data [8], and can be combined with the original rolling system to improve the performance [9]. Different from the traditional analysis methods, the artificial intelligence method can simulate the human brain to deal with the real process. Through data input, it can carry out self-learning training and simulation. It has a high degree of fitting and has significant advantages in dealing with non-linear relations. [PERSON] used a genetic algorithm to optimize the number of hidden layer nodes and network weights of the neural network, which improved the model accuracy and computational efficiency [10]. [PERSON] proposed a neural network control method integrating the genetic algorithm, which effectively improves the learning efficiency and convergence accuracy of the weight coefficients of the multi-layer feedforward neural network [11]. [PERSON] utilized the genetic algorithm to optimize the weight threshold of the multi-layer feedforward neural network, which improved the accuracy of the rolling force prediction model [12].