Browse > Article

Development of Stereo Vision Based Welding Quality Inspection System for RV Sinking Seat  

Yun, Sang-Hwan (공주대학교 기계공학과)
Kim, Han-Jong (한국기술교육대학교 정보기술공학부)
Kim, Sung-Gaun (공주대학교 기계자동차공학부)
Publication Information
Transactions of the Korean Society of Machine Tool Engineers / v.17, no.3, 2008 , pp. 71-77 More about this Journal
Abstract
This paper presents a stereo vision based autonomous inspection system for welding quality control of a RV(Recreational Vehicle) sinking seat. The three dimensional geometry of the welding bead, which is the welding quality criteria, is measured by using the captured stereo images with a median filter applied on it. The image processing software for the system was developed using the NI LabVTEW software with NI vision system. In the manufacturing process of a RV sinking seat, the developed system can be used for overcoming the precision error that arises from a visible inspection by an operator. The welding quality inspection system for RV sinking seat was verified using experimentation.
Keywords
sinking seat; welding quality inspection; stereo vision system;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Velasquez, J. D., and Shimon, Y. N., 2008, "Integration of Machine-Vision Inspection Information for Best-Matching of Distributed Components and Suppliers," Computers in Industry, Vol. 59, No. 1, pp. 69-81   DOI   ScienceOn
2 Kazantsev, I.G., Lemahieu, I., Salov, G.I., and Denys, R., 2002, "Statistical Detection of Defects in Radiographic Images in Nondestructive Testing," Signal Processing, Vol. 82, No. 5, pp. 791-801   DOI   ScienceOn
3 Park, Y. W., Park, H., Rhee, S. H., and Kang, M. J., 2002, "Real Time Estimation of $CO_2$ Laser Weld Quality for Automotive Industry," Optics & Laser Technology, Vol. 34, No. 2, pp. 135-142   DOI   ScienceOn
4 National Instrument, NI Vision Concepts Manual., viewed July 2007,
5 Romeu, R. S., Luiz, C. P., Siqueira, M., and Rebello, J., 2004, "Pattern Recognition of Weld Defects Detected by Radiographic Test," NDT & E International, Vol. 37, No. 6, pp. 461-470   DOI   ScienceOn
6 Jain, R., Kasturi, R., and Schunck, B.G., 1995, Machine Vision, McGRAW-HILL, Singapore, pp. 289-291
7 Prasanthi, G., Jin, C., Jeannine, G., and Ernest, L. H., 2000, Machine Vision Fundamentals, Marcel Dekker, New York, pp. 1-43
8 Yun, S. H., Kim, H. J., and Kim, S. G., 2008, "Development of Welding Quality Inspection System for RV Sinking Seat," Journal of Institute of Control, Robotics and Systems, Vol. 14, No. 1, pp. 75-80   DOI
9 Alaknanda., Anand, R.S., and Pradeep, K., 2006, "Flaw Detection in Radiographic Weld Images Using Morphological Approach," NDT & E International, Vol. 39, No. 1, pp. 29-33   DOI   ScienceOn
10 Shafeek, H.I., Gadelmawla, E.S., Abdel-Shafy, A.A., and Ellewa, I.M., 2004, "Automatic Inspection of Gas Pipeline Welding Defects Using an Expert Vision System," NDT & E International, Vol. 37, No. 4, pp. 301-307   DOI   ScienceOn
11 Golnabi, H., and Asadpour. A., 2007, "Design and Application of Industrial Machine Vision Systems," Robotics and Computer-Integrated Manufacturing, Vol. 23, No. 6, pp. 630-637   DOI   ScienceOn
12 Lashkia, V., 2001, "Defect Detection in X-ray Images Using Fuzzy Reasoning," Image and Vision Computing, Vol. 19, No. 5, pp. 261-269   DOI   ScienceOn