基于机器视觉的中型和重型板材的Camber智能控制方法的研究.
Chunyu He1, Chunpo Yue1, Zhong Zhao1
1State Key Laboratory of Digital Steel, Northeastern University, Shenyang 110819, China.
Materials (Basel, Switzerland)
|December 31, 2025
概括
这项研究引入了一个智能框架,用于检测和控制钢板的缺陷,即钢板的缺陷. 通过将机器学习预控制与机器视觉反相结合,它显著改善了钢板的平面性并降低了生产成本.
科学领域:
- 材料科学 材料科学 材料科学
- 制造业 工程 制造工程
- 人工智能的人工智能
背景情况:
- 凸是中型和重型钢板的关键缺陷,影响产品质量并增加运营成本.
- 由于复杂的影响因素和直接建模中的困难,控制曲是具有挑战性的.
- 钢铁行业的智能制造需要提高质量控制和精度.
研究的目的:
- 为中型和重型钢板提出一种新的曲率检测和控制方法.
- 开发一个智能控制框架,整合数据驱动的预先控制和基于机器视觉的反控制.
- 为了减少曲的发生,并确保钢板在制过程中的平坦度.
主要方法:
- 利用Optuna-XGBoost模型对板生产数据进行训练,以获得最佳的预控制值.
- 开发了一种基于机器视觉的技术,用于在制过程中实时检测凸.
- 建立了一个反控制模型,用于使用远距离横向运动进行凸转.
主要成果:
- 奥普图纳-XGBoost模型表现出极好的装配性能,R2值为0.9999 (训练) 和0.9794 (测试).
- 综合预控制和反控制方法有效减少了曲的发生.
- 在整个制过程中确保了钢板的整体平坦度.
结论:
- 已经建立了板的智能控制框架,将数据驱动的预控制和机器视觉反协同使用.
- 这种方法为钢铁制造业中复杂的非线性工业过程的在线最佳控制提供了一种新的解决方案.
- 该研究提高了铁钢行业智能制造的质量控制和精度.
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