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A Weld Joint Type Identification Method for Visual Sensor Based on Image Features and SVM.

Jiang Zeng1, Guang-Zhong Cao1, Ye-Ping Peng1

  • 1Shenzhen Key Laboratory of Electromagnetic Control, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China.

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|January 18, 2020
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Summary
This summary is machine-generated.

This study introduces an automated weld joint identification method using visual sensors and support vector machines (SVM). The novel approach achieves 98.4% accuracy, significantly improving welding automation efficiency.

Keywords:
image feature extractionsupport vector machine (SVM)visual sensorweld joint type identification

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Area of Science:

  • Robotics
  • Computer Vision
  • Materials Science

Background:

  • Welding robotics rely on visual sensors (camera and laser) for precision and stability.
  • Diverse workpieces result in various weld joint types, requiring different algorithms and parameters.
  • Manual adjustment of algorithms and parameters for each weld joint type is inefficient.

Purpose of the Study:

  • To develop an automated weld joint identification method for visual sensors.
  • To enhance the efficiency and automation of welding systems.
  • To address limitations in accuracy and applicability of existing identification methods.

Main Methods:

  • Proposed a novel weld joint identification method using image features and Support Vector Machine (SVM).
  • Utilized laser deformation around weld joints as the primary recognition information.
  • Extracted two types of features to create feature vectors for SVM model training.

Main Results:

  • Achieved a weld joint identification accuracy rate of 98.4%.
  • Demonstrated the validity and robustness of the proposed method through comparative and robustness testing experiments.
  • The optimal SVM model was established based on extracted feature vectors.

Conclusions:

  • The proposed method significantly improves the efficiency and automation of welding systems.
  • The SVM-based approach offers high accuracy and robustness for weld joint identification.
  • This advancement is crucial for intelligent welding robotics.