Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks. (September 2019) - Bacioiu, Daniel; Melton, Geoff; Papaelias, Mayorkinos; Shaw, Rob
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.