JOURNAL BROWSE
Search
Advanced SearchSearch Tips
A Study on Development of the Optimization Algorithms to Find the Seam Tracking
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
  • Journal title : Journal of Welding and Joining
  • Volume 34, Issue 2,  2016, pp.59-66
  • Publisher : The Korean Welding and Joining Society
  • DOI : 10.5781/JWJ.2016.34.2.59
 Title & Authors
A Study on Development of the Optimization Algorithms to Find the Seam Tracking
Jin, Byeong-Ju; Lee, Jong-Pyo; Park, Min-Ho; Kim, Do-Hyeong; Wu, Qian-Qian; Kim, Il-Soo; Son, Joon-Sik;
  PDF(new window)
 Abstract
The Gas Metal Arc(GMA) welding, called Metal Inert Gas(MIG) welding, has been an important component in manufacturing industries. A key technology for robotic welding processes is seam tracking system, which is critical to improve the welding quality and welding capacities. The objectives of this study were to develop the intelligent and cost-effective algorithms for image processing in GMA welding which based on the laser vision sensor. Welding images were captured from the CCD camera and then processed by the proposed algorithm to track the weld joint location. The proposed algorithms that commonly used at the present stage were verified and compared to obtain the optimal one for each step in image processing. Finally, validity of the proposed algorithms was examined by using weld seam images obtained with different welding environments for image processing. The results proved that the proposed algorithm was quite excellent in getting rid of the variable noises to extract the feature points and centerline for seam tracking in GMA welding and could be employed for general industrial application.
 Keywords
GMA welding process;Seam tracking;Image process;Laser vision sensor;
 Language
Korean
 Cited by
 References
1.
S. Y. Liu, G. R. Wang, H. Zhang and J. P. Jia, Design of Robot Welding Seam Tracking System With Structured Light Vision, Chinese Journal of Mechanical Engineer, 23(4) (2010), 436-442 crossref(new window)

2.
I. S. kim, B. Y. Kang, A Study on Development of Sensing System for Welding Automation, Journal of KWJS, 24(3), 2006.6, 9-14 (in Korean)

3.
W. Huang and R. Kovacevic, A Laser-Based Vision System for Weld Quality Inspection, Sensors, 11 (2011), 506-521 crossref(new window)

4.
H. H. Kim, I. S. Kim, C. U. Park, J. S. Son, J. H. Seo, J. W. Jung, G. S. Jeon, Development of Weld Automation Equipments Using the Infrared Temperature Sensor, Proceedings of KWJS, 46(2006), 301-303 (in Korean)

5.
H. Hwang and R. A. Haddad, Adaptive Median Filters: New Algorithms and Results, IEEE Transactions on Image Processing, 4 (1995), 499-502 crossref(new window)

6.
V. R. Vijaykumar, P. T. Vanathiand and P. Kanagasabapathy, Fast and Efficient Algorithm to Remove Gaussian Noise in Digital Images, International Journal of Computer Science, 37(2010), 122-125

7.
G. Gupta, Algorithm for Image Processing Using Improved Median Filter and Comparison of Mean, Median and Improved Median Filter, International Journal of Soft Computing and Engineering, 1(2011), 304-311

8.
F. A. Dawood,R. W. Rahmat, S. B. Kadiman, L. N. Abdullah and M. D. Zamrin, Effect Comparison of Speckle Noise Reduction Filters on 2D-Echocardigraphic Images, World Academy of Science, Engineering and Technology, 69 (2012), 425-430

9.
J. Gil and R. Kimmel, Efficient Dilation, Erosion, Opening and Closing Algorithms, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(12) (2002), 1606-1617 crossref(new window)

10.
M. Jankowski, Erosion, Dilation and Related Operators, Proceeding of 8th International Mathematical Symposium, 2006

11.
N. Otsu, A Threshold Selection Method from Gray-Level Histogram, IEEE Transactions on Systems, Man, and Cybernetics, SMC-9(1) (1979), 62-66

12.
H. Greenspan, M. Laifenfeld, S. Einav and O. Barnea, Evaluation of Center-Line Extraction Algorithms in Quantitative Coronary Angiography, IEEE Transactions on Medical Imaging, 20(9) (2001), 928-941 crossref(new window)

13.
O. Aichholzer, F. Aurenhammer, D. Alberts and B. Gatner, A Novel Type of Skeleton for Polygons, Journal of Universal Computer Science, 1(12) (1995), 752-761

14.
N. D. Cornea, D. Silver and P. Min, Curve-Skeleton Properties, Applications, and Algorithms, IEEE Transaction on Visualization and Computer Graphics, 13(3) (2007), 530-548 crossref(new window)

15.
C. Harris and M. Stephens, A Combined Corner and Edge Detector, Proceedings of 4th Alvey Vision Conference, (1988), 147-151

16.
M. N. Nobi and M. A. Yousuf, A New Method to Remove Noise in Magnetic Resonance and Ultrasound Images. Journal of Scientific Research, 3(1) (2011), 81-89

17.
R. Maini and H. Aggarwal, Performance Evaluation of Various Speckle Noise Reduction Filters on Medical Images, International Journal of Recent Trends in Engineering, 2(4) (2009), 22-25

18.
P. K. Sahoo, S. Soltani, A. K. C. Wongand and Y. Chen, A Survey of Thresholding Techniques, Comput. Graph. Image Process. 41 (1988), 233-260, 1988 crossref(new window)

19.
J. Z. Shen, Research on Seam Image Processing for $CO_2$ Horizontal Position Welding Based on Laser Vision Sensing, PhD thesis, Department of Materials Processing Engineering, Tianjin University, 2010