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 dataset11https://www.kaggle.com/danielbacioiu/tig-aluminium-5083. 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.
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