Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks

Daniel Bacioiu, Geoff Melton, Mayorkinos Papaelias, Rob Shaw

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)
154 Downloads (Pure)

Abstract

Weld defect identification represents one of the most desired goals in the field of non-destructive testing (NDT) of welds. The current study investigates a system for assessing tungsten inert gas (TIG) welding using a high dynamic range (HDR) camera with the help of artificial neural networks (ANN) for image processing. This study proposes a new dataset1 of images of the TIG welding process in the visible spectrum with improved contrast, similar to what a welder would normally see, and a model for computing a label identifying the welding imperfection. The progress (accuracy) achieved with the new system over varying degrees of categorisation complexity is thoroughly presented.
Original languageEnglish
Pages (from-to)603-613
Number of pages11
JournalJournal of Manufacturing Processes
Volume45
Early online date7 Aug 2019
DOIs
Publication statusPublished - 1 Sep 2019

Fingerprint

Dive into the research topics of 'Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks'. Together they form a unique fingerprint.

Cite this