Automated defect classification of SS304 TIG welding process using visible spectrum camera and machine learning

Daniel Bacioiu, Geoff Melton, Mayorkinos Papaelias, Rob Shaw

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)
278 Downloads (Pure)

Abstract

Tungsten Inert Gas welding is dependent on human supervision, it has an emphasis on visual assessment, and it is performed in a controlled environment, making it suitable for automation. This study designs a system for assessing the tungsten inert gas welding quality with the potential of application in real-time. The system uses images in the visible spectrum paired with the state-of-the-art approach for image classification. The welding images represent the weld pool in visible spectra balanced using high dynamic range technology to offset the powerful arc light. The study trains models on a new tungsten inert gas welding dataset, leveraging the state-of-the-art machine learning research, establishing a correlation between the aspect of the weld pool and surrounding area and the weld quality, similar to an operator's assessment.
Original languageEnglish
Article number102139
Number of pages9
JournalNDT & E International
Volume107
Early online date6 Jul 2019
DOIs
Publication statusPublished - 1 Oct 2019

Keywords

  • Weld monitoring
  • High dynamic range camera
  • Machine learning
  • Vision
  • Automation

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