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07-08-2018 | S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing | Issue 5/2019

Neural Computing and Applications 5/2019

Research on hot-rolling steel products quality control based on BP neural network inverse model

Journal:
Neural Computing and Applications > Issue 5/2019
Authors:
Shiyi Xing, Jianguo Ju, Jinsheng Xing

Abstract

Taking the hot-rolling products made by an iron and steel company as the research object, this paper builds the inverse model reflecting the relationship between the hot-rolling steel product performance indicators, the chemical composition of steel and the rolling technological parameters by using the BP neural network. So the purpose of getting technological parameters is achieved, according to the given steel performance indicators. Combining the BP neural network, adaptive inverse control with internal model control theory, this paper builds the BP neural network inverse model with multiple input and single output based on internal model control. Therefore, it realizes the inverse mapping between the output and the input variables of the BP neural network. And the output variables can be obtained according to the input variables. Besides, this paper also gives the detailed steps to solve the inverse model. Then, the model is applied to the hot-rolling steel products quality control system. The performance indicators of the hot-rolling products are set up, and the rolling technological parameters—the rolling crimp temperature—are solved. The model realizes the controllability of rolling technological parameters. Finally, through the verification of hot-rolling products quality control positive system, the error is in line with the enterprise production requirements.

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