Automation and Autonomous Systems

G. Senthil Kumar ¹, U. Natarajan ², G. Sankaranarayanan ³

Affiliations
1 Department of Mechanical, Velammal College of Engineering & Technology, Madurai, Tamil Nadu, IN
2 Department of Mechanical, A. C College of Engineering & Technology, Karaikudi, Tamil Nadu, IN
3 Department of Mechanical, Raja College of Engineering & Technology, Madurai, Tamil Nadu, IN

Abstract

Metal Inert Gas (MIG) welding is one of the major metal joining process used to fabricate many engineered artifacts and structure such as cars, ships, space shuttles and pipe lines. Flaws resulted from welding operations are detrimental to the integrity of the fabricated artifacts or structure. Although the welding process is carried out as manually or automatically, flaws are formed during the welding operations.These flaws include lack of fusion, porosities, cracks, lack of penetration, excess weld, insufficient weld, inclusions, gas holes etc. To maintain the desirable level of structural integrity, welds must be inspected according to the established standards. In this paper, a machine vision system is introduced to extract the various features of the MIG welded joint by capturing image through CCD camera with proper illumination, and then various image processing techniques and classifier is used to calssify the defects accoriding to the international standards. This vision system is connected to the host computer and classification is done by artificial neural network based on predefined one. In this proposed method, a comparison is made between the accuracy of the single image by turn on four zones LEDs of the illumination at a time with the accuracy of the multiple images by the zones LEDs are turned on, one after the other This proposed method enables overall accuracy of the four zones of the images as 95% from the 40 samples of the welded images and finally parameters are used to evaluate the performance of the proposed system.

Keywords

MIG Welding,Welding Defects, Vision System, Feature Extraction.

Full Text

     

G. Senthil Kumar ¹, U. Natarajan ², M. Srinivasagan ²

Affiliations
1 Dept. of Mech., Velammal College of Engg & Tech., Madurai, Tamil Nadu, IN
2 Dept. of Mech., A. C College of Engg & Tech., Karaikudi, Tamil Nadu, IN

Abstract

In this paper, an efficient technique has been described for inspection of Metal Inert Gas welding (MIG). A machine vision system has been developed for identifying and classifying the surfaces of butt joint as per standard EN25817 in MIG welding.Images of welded surfaces are captured through CCD camera. Then regions of interest are segmented and the average gray levels of the characteristic features of these images are calculated using 2D feature vector and Gaussian distribution based features. Finally, welded joints can be classified into one of the four pre-defined images based on the back propagation neural network. In this work, 80 real samples of images are tested and performance of the vision system is compared with twodifferent feature extractions. vision inspection system using Gaussian based feature extraction method produced 93.75% than 2D feature extraction method which is produced 92.5%.

Keywords

Machine Vision, Weld Classification, Industrial Inspection, Back Propagation Neural Network (BPN).

Full Text

     

G. Senthil Kumar ¹, U. Natarajan ², G. Sankaranarayanan ³

Affiliations
1 Department of Mechanical, Velammal College of Engineering & Technology, Madurai, Tamil Nadu, IN
2 Department of Mechanical, A. C. College of Engineering & Technology, Karaikudi, Tamil Nadu, IN
3 Department of Mechanical, Raja College of Engineering & Technology, Madurai, Tamil Nadu, IN

Abstract

Metal Inert Gas (MIG) welding is one of the major metal joining process used to fabricate many engineered artifacts and structure such as cars, ships, space shuttles and pipe lines. Flaws resulted from welding operations are detrimental to the integrity of the fabricated artifacts or structure. Although the welding process is carried out as manually or automatically, flaws are formed during the welding operations.These flaws include lack of fusion, porosities, cracks, lack of penetration, excess weld, insufficient weld, inclusions, gas holes etc. To maintain the desirable level of structural integrity, welds must be inspected according to the established standards. In this paper, a machine vision system is introduced to extract the various features of the MIG welded joint by capturing image through CCD camera with proper illumination, and then various image processing techniques and classifier is used to calssify the defects accoriding to the international standards .This vision system is connected to the host computer and classification is done by artificial neural network based on predefined one. In this proposed method, a comparison is made between the accuracy of the single image by turn on four zones LEDs of the illumination at a time with the accuracy of the multiple images by the zones LEDs are turned on, one after the other This proposed method enables overall accuracy of the four zones of the images as 95% from the 40 samples of the welded images and finally parameters are used to evaluate the performance of the proposed system.

Keywords

MIG Welding, Welding Defects, Vision System, Feature Extraction.

Full Text