At present, the production of steel plate and strip plays an important role in the national economy. The thickness and shape of steel strip are the main indicators of the accuracy of steel strip products, and the rolling plan is the production belt. This is the factory of production capacity, which has realized high-precision product quality and high-quality form and main technical content. With the rapid development of rolling technology, the rolling schedule based on experience can no longer meet the needs of production, making the development of cold rolling mill use technology, It should be combined with practical production experience at the theoretical level, and what makes it have universal guiding significance.
Based on the consideration of equipment and product quality, according to the multiple objectives of rolling force, strip shape and reduction rate, the capacity of the function of load distribution sets the power and tension, and becomes an unlimited optimization measure through the problem of optimization constraints. The two-step calculation method is adopted. In the initial stage, the method of factor theory is used to solve the nonlinear equation, and in the optimization stage, the simple form method is used to solve the multi-objective function, At the same time, the storage table of mobile specification is established, which provides convenience for the establishment of mobile schedule.
Various control models and of the continuous rolling mill are carefully analyzed, and various parameter models suitable for the rolling program are put forward. In view of the shortcomings of the theoretical model of rolling force, the data network model is established by using the self-learning ability of the data network to predict the rolling pressure. Considering the model parameters and rolling conditions and time performance, it is suggested that the adaptive method of the parameter model changes in the rolling process, A method to correct the rolling force adaptive problem is provided.
According to different materials and equipment classification, a large number of statistics are carried out, the self-learning parameters of various specification models are stored in sequence, and the accuracy of configuration is improved through adaptive and off-line self-learning. This load distribution calculation method has been successfully applied to the process control system of five stand cold rolling mill.
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