TY - JOUR
T1 - Automated defect classification of Aluminium 5083 TIG welding using HDR camera and neural networks
AU - Bacioiu, Daniel
AU - Melton, Geoff
AU - Papaelias, Mayorkinos
AU - Shaw, Rob
PY - 2019/9/1
Y1 - 2019/9/1
N2 - 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.
AB - 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.
U2 - 10.1016/j.jmapro.2019.07.020
DO - 10.1016/j.jmapro.2019.07.020
M3 - Article
SN - 1526-6125
VL - 45
SP - 603
EP - 613
JO - Journal of Manufacturing Processes
JF - Journal of Manufacturing Processes
ER -