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Research on Intelligent Control Method of Camber for Medium and Heavy Plate Based on Machine Vision.

Chunyu He1, Chunpo Yue1, Zhong Zhao1

  • 1State Key Laboratory of Digital Steel, Northeastern University, Shenyang 110819, China.

Materials (Basel, Switzerland)
|December 31, 2025
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Summary

This study introduces an intelligent framework to detect and control camber, a defect in steel plates. By combining machine learning pre-control with machine vision feedback, it significantly improves steel plate flatness and reduces production costs.

Keywords:
camberfeedback controlimage processingmachine learningplate

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Area of Science:

  • Materials Science
  • Manufacturing Engineering
  • Artificial Intelligence

Background:

  • Camber is a critical defect in medium and heavy steel plates, impacting product quality and increasing operational costs.
  • Controlling camber is challenging due to complex influencing factors and difficulties in direct modeling.
  • Intelligent manufacturing in the iron and steel industry demands enhanced quality control and precision.

Purpose of the Study:

  • To propose a novel camber detection and control method for medium and heavy steel plates.
  • To develop an intelligent control framework integrating data-driven pre-control and machine vision-based feedback control.
  • To reduce the occurrence of camber and ensure the flatness of steel plates during the rolling process.

Main Methods:

  • Utilized the Optuna-XGBoost model to train on plate rolling production data for optimal pre-control values.
  • Developed a machine vision-based technology for real-time camber detection during the rolling process.
  • Established a feedback control model for camber using distal lateral movement.

Main Results:

  • The Optuna-XGBoost model demonstrated excellent fitting performance with R² values of 0.9999 (training) and 0.9794 (test).
  • The integrated pre-control and feedback control approach effectively reduced camber occurrence.
  • Ensured the overall flatness of steel plates throughout the rolling process.

Conclusions:

  • An intelligent control framework for plate camber has been established, synergizing data-driven pre-control and machine vision feedback.
  • This approach offers a novel solution for the online optimal control of complex nonlinear industrial processes in steel manufacturing.
  • The study enhances quality control and precision in intelligent manufacturing for the iron and steel industry.