> Details

Al-Karkhi, Nazar Kais [Author]; Abbood, Wisam T. [Author]; Khalid, Enas A. [Author]; Al-Tamimi, Adnan Naji Jameel [Author]; Kudhair, Ali A. [Author]; Abdullah, Oday Ibraheem [Author] ; Technische Universität Hamburg, Technische Universität Hamburg Institut für Laser- und Anlagensystemtechnik

Intelligent robotic welding based on a computer vision technology approach

Reference
management

Bookmarks

Remove from
bookmarks

Share this on Whatsapp

Close

Bookmarks



You can manage bookmarks using lists, please log in to your user account for this.
  • Media type: E-Article
  • Title: Intelligent robotic welding based on a computer vision technology approach
  • Contributor: Al-Karkhi, Nazar Kais [VerfasserIn]; Abbood, Wisam T. [VerfasserIn]; Khalid, Enas A. [VerfasserIn]; Al-Tamimi, Adnan Naji Jameel [VerfasserIn]; Kudhair, Ali A. [VerfasserIn]; Abdullah, Oday Ibraheem [VerfasserIn]
  • Corporation: Technische Universität Hamburg ; Technische Universität Hamburg, Institut für Laser- und Anlagensystemtechnik
  • imprint: 2022
  • Published in: Computers ; 11(2022), 11 vom: 29. Okt., Artikel-ID 155, Seite 1-23
  • Language: English
  • DOI: 10.15480/882.4733; 10.3390/computers11110155
  • ISSN: 2073-431X
  • Identifier:
  • Keywords: robotic welding ; image processing ; ANFIS ; line detection
  • Origination:
  • Footnote: Sonstige Körperschaft: Technische Universität Hamburg
    Sonstige Körperschaft: Technische Universität Hamburg, Institut für Laser- und Anlagensystemtechnik
  • Description: Robots have become an essential part of modern industries in welding departments to increase the accuracy and rate of production. The intelligent detection of welding line edges to start the weld in a proper position is very important. This work introduces a new approach using image processing to detect welding lines by tracking the edges of plates according to the required speed by three degrees of a freedom robotic arm. The two different algorithms achieved in the developed approach are the edge detection and top-hat transformation. An adaptive neuro-fuzzy inference system ANFIS was used to choose the best forward and inverse kinematics of the robot. MIG welding at the end-effector was applied as a tool in this system, and the weld was completed according to the required working conditions and performance. The parts of the system work with compatible and consistent performances, with acceptable accuracy for tracking the line of the welding path.
  • Access State: Open Access
  • Rights information: Attribution (CC BY)