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Updated: Feb 16, 2026

Ultrasonic Welding of Thermoplastic Composite Coupons for Mechanical Characterization of Welded Joints through Single Lap Shear Testing
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A Weld Position Recognition Method Based on Directional and Structured Light Information Fusion in

Jinle Zeng1,2, Baohua Chang3, Dong Du4

  • 1Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China. zengjinle@casicloud.cn.

Sensors (Basel, Switzerland)
|January 6, 2018
PubMed
Summary

This study introduces a new visual method for detecting weld pass positions in multi-layer/multi-pass welding (MLMPW). The technique fuses directional and structured light imaging for accurate robotic seam tracking.

Keywords:
information fusionmulti-layer/multi-pass weldingseam trackingvisual detection

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Area of Science:

  • Robotics and Automation
  • Materials Science and Engineering
  • Computer Vision

Background:

  • Multi-layer/multi-pass welding (MLMPW) is crucial for joining thick components in the energy sector.
  • Accurate real-time weld pass recognition is essential for automated robotic welding path planning and deviation correction.
  • Existing methods struggle with MLMPW due to subtle geometrical differences between adjacent weld passes.

Purpose of the Study:

  • To develop a novel weld position detection method specifically for MLMPW.
  • To enable precise path planning and real-time seam tracking in automated welding processes.
  • To overcome limitations of current visual recognition technologies in MLMPW.

Main Methods:

  • Developed a synchronous acquisition method for directional and structured light imaging.
  • Implemented image fusion techniques to eliminate interference between adjacent weld passes.
  • Fused directional and structured light data to determine the 3D positions of weld passes.

Main Results:

  • Achieved weld position detection with a deviation of less than 0.6 mm.
  • Each detection process is completed within 30 ms.
  • Demonstrated the feasibility of the method for complex welding scenarios.

Conclusions:

  • The proposed fusion method effectively detects weld pass positions in MLMPW.
  • This technology supports automated path planning and seam tracking for robotic MLMPW.
  • The method is also applicable to electron beam freeform fabrication processes.