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Vision Inspection System for MIG Welding Joints using Different Feature Extraction Methods
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
References
H.I.Shafeek, E.S.Gadelmawla, A.A.Abdel-Shafy, I.M.Elewa(2004) Assessment of welding defects for gas pipeline radiographs using computer vision. NDT&E International 37:291-299.
H.I.Shafeek, E.S.adelmawla, A.A.Abdel-Shafy, I.M.Elewa(2004)Automatic inspection of gas pipeline welding defects using an expertvision system . NDT&E International 37:301-307.
Tae-Hyeon Kim,Tai-Hoon Cho, Young Shik Moon,Sung Han Park(1999) Visual inpection system for the classification of solder joints. PatternRecognition 32: 565-575.
S. Jagannathan(1997) Automatic inspection of wave soldered joints using
neural networks.Journal of Manufacturing Systems. Vol.16/No.6
Jagannathan.S(1990) Intelligent Inspection of wave soldered Joints -
Technical Report. Journal of Manufacturing Systems. Vol.11/No.2
Elias N. Malamas,Euripides G.M. Petrakis, Michalis Zervakis, Laurent
Petit(2003) A survey on industrial vision systems, applications and tools.Image and computing 21: 171-188.
T.W.Liao, D.-M. Li, Y.-M. Li (1999) Detection of welding flaws from radiographic images with fuzzy clustering methods.Fuzzy sets and systems 108:145-158.
T. Waren Liao, K. Tang (1997) Automated extraction of welds from
digitized radiographic images based on MLP neural networks. Applied artificial intelligence,11:197-218
T. Waren Liao,Damin Li, Yueming Li(2000) Extraction of welds from radiographic images using fuzzy classifiers. Information sciences 126:21- 40.
V. Lashkia (2001) Defect detection in X-ray images using fuzzy reasoning. Image and vision computing 19: 261-269.
M. Gauss, A. Buerkle, T. Laengle, H.Woern, J. Stelter, S. Ruhmkorf, R. Middelmann(2003) Adaptive robot based visual inspection of complex parts. ISR2003.
T.W.Liao(2003) Classification of welding flaw types with fuzzy expertsystems. Expert systems with application25:101-111.
Romeu R. da Silva, Luiz P. Caloba, Marcio H.S. Siqueira, Joao M.A. Rebello(2004) Pattern recognition of weld defects detected by radiographic test. NDT&E International 37: 461-470.
Rafael Vilar, Juan Zapata, Ramon Ruiz (2009). An automatic system of classification of weld defects in radiographic images. NDT&E
International 42: 467-476.
Yan Wang, Yi Sun, Peng Lv, Hao Wang (2008) Detection of line weld defects based on multiple thresholds and support vector machine. NDT&E International 41: 517-524.
E.S.Amin (2008) Application of artificial neural networks to evaluate weld defects of nuclear components,Journal of nuclear and radiation physics,Vol.3,No.2,pp.83-92
Joongho Chang et al.,Cork Quality classification system using a unified image processing and fuzzy-Neural Network Methodology,IEEE
Transactions on neural networks,Vol.8,No.4,july 1997.
DOI: http://dx.doi.org/10.36039/AA032012005
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