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Active visual continuous seam tracking based on adaptive feature detection and particle filter tracking.

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    Applied Optics
    |June 10, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an advanced active vision sensing method for gas metal arc welding (GMAW) seam tracking. The system achieves high accuracy and speed for intelligent welding production, even with interference.

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    Area of Science:

    • Robotics and Automation
    • Computer Vision
    • Manufacturing Engineering

    Background:

    • Intelligent production relies on precise welding seam tracking.
    • Existing methods struggle with adaptability, accuracy, and robustness in weld feature detection.
    • Gas metal arc welding (GMAW) requires reliable online seam tracking for efficiency.

    Purpose of the Study:

    • To develop an online welding seam tracking method for GMAW using active vision sensing.
    • To improve the accuracy, adaptability, and robustness of weld feature point detection.
    • To enable real-time, high-precision tracking for intelligent welding robots.

    Main Methods:

    • Utilized the Steger sub-pixel detection method for accurate feature extraction.
    • Developed self-adaptive search windows and slope extraction techniques for real-time weld detection.
    • Employed a particle filter to predict weld position during interference (e.g., arc light).

    Main Results:

    • Achieved a detection speed of 27 ms.
    • Reached detection accuracy of 0.03 mm and tracking accuracy of 0.78 mm.
    • Demonstrated effectiveness on butt, fillet, and lap welds using a laser vision sensing robot platform.

    Conclusions:

    • The proposed active vision sensing method significantly enhances GMAW seam tracking performance.
    • The system meets the stringent accuracy and speed requirements for intelligent welding applications.
    • This approach offers a robust solution for real-time weld detection and tracking in automated manufacturing.