Detection and Quantification of Tungsten Inclusion in Weld Thermographs for On-line Weld Monitoring by Region Growing and Morphological Image Processing Algorithms

Tungsten Inert Gas Welding is the best suited welding technique for precision welding in Atomic and aircraft industries. The most commonly occurring weld defect is Tungsten Inclusion, which is mainly due to high welding current. Monitoring and controlling weld current can avoid the defect. Developing an automated on-line welding system to correct the deviation in the welding current requires an effective image-processing algorithm to extract defect features from the sensor output image. Infrared Thermography is the best-suited sensor for on-line weld monitoring. Conventional feature extraction algorithms are parameter dependent, image dependent and time consuming thereby making it unsuitable for on-line monitoring. This paper proposes Region growing and morphological image processing algorithm to identify and quantify the weld defect from thermographs. Defect is quantified by major axis length, minor axis length and area. It takes 0.406 seconds in contrast to 3.484 seconds of the conventional algorithm in a Pentium IV system with a processor speed of 2.64GHZ system. Keywords: Thermographs, Tungsten Inclusion, Seed, Threshold, feature vector