Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks. (September 2019)

• Record Type: Journal Article
• Authors: Bacioiu, Daniel; Melton, Geoff; Papaelias, Mayorkinos; Shaw, Rob
• Is Part Of: Journal of manufacturing processes. Volume 45(2019)
• Page Start: 603
• Page End: 613
• Publication Date: 2019-09
• Subjects: Automation -- Convolutional neural networks -- HDR camera -- Vision -- Process monitoring -- Quality assessment; Production management -- Data processing -- Periodicals; Manufacturing processes -- Periodicals; Procestechnologie; Productietechniek; Production -- Gestion -- Informatique -- Périodiques; Fabrication -- Périodiques; Manufacturing processes; Production management -- Data processing; Periodicals; 670.5
• DOI: 10.1016/j.jmapro.2019.07.020
• Languages: English
• ISSNs: 1526-6125

Abstract

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 dataset 1 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.