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

  • Shiyi Xing
  • Jianguo JuEmail author
  • Jinsheng XingEmail author
S.I. : Emerging Intelligent Algorithms for Edge-of-Things Computing


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.


BP neural network Adaptive inverse control Internal model control Hot-rolling products Quality control 



This project was supported by Shanxi Soft Science Fund Project of China (2011041033-03). The authors would like to express thanks for the anonymous reviewers’ and the editors’ instructive suggestions which to a great degree improved the quality of this paper.

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.


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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.College of Information EngineeringNorthwest A&F UniversityXi’anChina
  2. 2.School of Mathematics and Computer ScienceShanxi Normal UniversityLinfen CityChina

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