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Related Concept Videos

Steel Fastening Techniques01:17

Steel Fastening Techniques

303
Steel sections can be joined together through various fastening techniques including riveting, bolting, and welding, each suitable for different structural requirements and conditions.
Rivets are cylindrical steel fasteners with a specially designed head. During application, rivets are heated until white-hot and then inserted through pre-drilled holes in the steel sections. A pneumatic hammer is used to shape the exposed end into a second head, securing the sections together.
Bolting is another...
303

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Semi-Supervised Training for Positioning of Welding Seams.

Wenbin Zhang1, Jochen Lang1

  • 1School of EECS, University of Ottawa, Ottawa, ON K1N 6N5, Canada.

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|November 13, 2021
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Summary

This study introduces a novel semi-supervised learning algorithm for robotic welding seam detection. The method enhances accuracy and robustness using minimal labeled data, improving key-point detection for precise seam placement.

Keywords:
localizationsemi-supervised learningwelding seam

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Area of Science:

  • Robotics
  • Computer Vision
  • Machine Learning

Background:

  • Robotic welding relies on vision-based systems for seam placement.
  • Traditional machine vision methods struggle with manufacturing and imaging variations.
  • Supervised deep learning requires extensive labeled data, which is costly and time-consuming.

Purpose of the Study:

  • To develop a semi-supervised learning algorithm for robust key-point detection in robotic welding seam placement.
  • To improve accuracy and robustness without extensive manual data labeling.
  • To address the limitations of traditional methods in dynamic manufacturing environments.

Main Methods:

  • A novel semi-supervised learning algorithm for key-point detection was developed.
  • The algorithm was designed to work with a minimal number of labeled images (as few as fifteen).
  • Full image resolution was utilized to enhance key-point detection accuracy.

Main Results:

  • The proposed semi-supervised approach demonstrated robustness with limited labeled data.
  • The method achieved high accuracy in detecting key-points for welding seam placement.
  • The algorithm effectively handled variations in manufacturing and imaging conditions.

Conclusions:

  • Semi-supervised learning offers a viable solution for accurate and robust robotic welding seam detection.
  • The developed algorithm significantly reduces the need for expert data labeling.
  • This approach enhances the reliability and efficiency of automated welding processes.