• Senthil Kumar G Dept. of Mech. Velammal College of Engg & Tech. Madurai, Tamil Nadu, India
  • Natarajan U Dept. of Mech. A. C College of Engg & Tech., Karaikudi, Tamil Nadu, India
  • Athijayamani A Dept. of Mech. A. C College of Engg & Tech., Karaikudi, Tamil Nadu, India
  • Srinivasagan M Dept. of Mech. A. C College of Engg & Tech., Karaikudi, Tamil Nadu, India


Machine Vision, Weld Classification, MIG Welding, Vision Inspection Back Propagation Neuralnetwork (BPN)


In this study, we use machine vision to inspection of welding surfaces produced by the MIG welding Process. Machine vision allows for the inspection of welded surfaces without touching or scratching the surface, and provides the flexibility for inspection parts. In this experimental work, inspection system  has been developed for identifying and classifying the surface defects of fillet joint as per standard EN25817 in Metal Inert Gas (MIG) welding. In this proposed vision system, images of welding surfaces are captured through CCD camera. From these images, the regions of interest are segmented and the average gray levels of the characteristic features of these images are calculated.. Finally, welded joints can be classified into one of the four pre-defined ones based on the back propagation neural network. This proposed system, 80  welded samples are analysed with two different feature extraction methods.


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How to Cite

Senthil Kumar G, Natarajan U, Athijayamani A, and Srinivasagan M, “AUTOMATIC INSPECTION FOR MIG WELDING JOINTS USING MACHINE VISION ”, JME, vol. 8, no. 3, pp. 156–168, Sep. 2013.