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This paper systematically reviews the technological evolution and fundamental principles of flatness detection for hot-rolled steel strips. It provides detailed analyses of principal technical approaches including multi-point laser triangulation, laser Moiré, laser light-section, projected fringe pattern, and 3D reconstruction methods, covering their measurement principles, typical instrumentation, and industrial implementations. Comparative studies demonstrate that traditional contact-based techniques are being progressively superseded by non-contact optical measurement methods, with 3D reconstruction technology emerging as a research focus due to its capability of acquiring comprehensive 3D topographic data of strip surfaces. The article emphasizes cutting-edge applications of artificial intelligence in flatness detection, particularly innovative integrations of neural networks, machine vision and deep learning algorithms. Finally, it summarizes existing technological challenges and proposes future development trends, highlighting multi-physics coupled measurement, intelligent compensation algorithms, and digital twin technologies as crucial research directions.

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Advances in Optical Measurement Technologies and AI Applications for Hot-Rolled Strip Flatness Detection

  • 会议论文

  • 发表日期：

    2026年

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  • Ziyi Zhang,
  • Xiaoyan Zhao,
  • Jianheng Zhang,
  • Zhaohui Zhang

摘要

This paper systematically reviews the technological evolution and fundamental principles of flatness detection for hot-rolled steel strips. It provides detailed analyses of principal technical approaches including multi-point laser triangulation, laser Moiré, laser light-section, projected fringe pattern, and 3D reconstruction methods, covering their measurement principles, typical instrumentation, and industrial implementations. Comparative studies demonstrate that traditional contact-based techniques are being progressively superseded by non-contact optical measurement methods, with 3D reconstruction technology emerging as a research focus due to its capability of acquiring comprehensive 3D topographic data of strip surfaces. The article emphasizes cutting-edge applications of artificial intelligence in flatness detection, particularly innovative integrations of neural networks, machine vision and deep learning algorithms. Finally, it summarizes existing technological challenges and proposes future development trends, highlighting multi-physics coupled measurement, intelligent compensation algorithms, and digital twin technologies as crucial research directions.