Plate strip steel convexity prediction method based on data driving and mechanism model fusion

Abstract

The invention belongs to the technical field of strip steel product quality control, and relates to a plate strip steel convexity prediction method based on data driving and mechanism model fusion, which comprises the following steps: establishing a hot continuous rolling outlet plate convexity mechanism model, combining the mechanism model with a DNN model to establish a strip steel convexity prediction DNN model, and taking a calculated value of the mechanism model as a reference value of the outlet plate convexity, using the deviation value of the reference value and the actual value of the outlet plate convexity as the output of the strip steel convexity prediction DNN model, and using the sum of the predicted value based on the strip steel convexity prediction DNN model and the reference value as the final plate strip steel convexity predicted value. According to the method, the deviation between the calculated value and the actual value is output as the DNN model, the prediction error range can be reduced, and guarantee is provided for more accurate plate shape control. At the present stage, a hot continuous rolling production line is perfect in the aspects of industrial data collection and storage, so that the method has high popularization capacity, and a new method is provided for improving the precision of the convexity of the strip steel outlet plate.

Description

technical field [0001] The invention belongs to the technical field of strip steel product quality control, and relates to a method for predicting the convexity of strip steel based on data-driven and mechanism model fusion. Background technique [0002] Hot-rolled strip occupies an important position in the entire industrial system, among which the flatness is a key indicator to measure whether the quality of hot-rolled strip is qualified, and flatness control has also become an important technology in strip production. In recent years, a lot of scientific research work has been carried out on the rolling process of hot strip rolling at home and abroad, such as the derivation and establishment of mathematical models, etc., but the actual rolling process is more complicated, with strong coupling, nonlinear, multivariable, etc. characteristics, there are uncertain unknown factors, it is difficult to establish an accurate mathematical model. Therefore, it is necessary to use ...

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Problems solved by technology

[0003] In the process of traditional hot continuous rolling exit plate crown prediction, the plate and strip crown is directly used as the output value of the neural network, and the benchmark value of the plate and strip crown is not set, and the parameters are predicted only by the neural network. The error range of its prediction is large, and the prediction accuracy of the model is reduced

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