Welding Groove Mapping - Implementation and Evaluation of Image Processing Algorithms on Shiny Surfaces

Cristiano Rafael Steffens, Bruno Quaresma Leonardo, Sidnei Carlos da Silva Filho, Valquiria Huttner, Vagner Santos da Rosa, Silvia Silva da Costa Botelho

Abstract

Electric arc welding is a key process in the heavy steel industries. It is a very complex task that demands a high degree of control in order to meet the international standards for fusion welding. We propose a Vision-Based Measurement (VBM) system and evaluate how different algorithms impact the results. The proposed system joins hardware and software to image the welding plates using a single CMOS camera, run computer vision algorithms and control the welding equipment. A complete prototype, using a commercial linear welding robot is presented. The evaluation of the system as a groove mapping equipment, considering different processing algorithms combined with noise removal and line segment detection techniques, allows us to define the appropriated approach for shop floor operation, combining low asymptotic cost and measurement quality.

References

  1. Akinlar, C. and Topal, C. (2011). EDLines: A real-time line segment detector with a false detection control. Pattern Recognition Letters, 32(13):1633-1642.
  2. Ang Jr, M. H., Lin, W., and Lim, S.-Y. (1999). A walk-through programmed robot for welding in shipyards. Industrial Robot: An International Journal, 26(5):377-388.
  3. BIPM, I., IFCC, I., ISO, I., and IUPAP, O. (2008). Evaluation of measurement data - guide to the expression of uncertainty in measurement.
  4. De Xu, Min Tan, X. Z. Z. T. (2004). Seam tracking and visual control for robotic arc welding based on structured light stereovision. International Journal of Automation and Computing, 1(1):63.
  5. Debevec, P. E. and Malik, J. (2008). Recovering high dynamic range radiance maps from photographs. In ACM SIGGRAPH 2008 classes, page 31. ACM.
  6. Dilthey, U. and Gollnick, J. (1998). Through the arc sensing in gma-welding with high speed rotating torch. In Industrial Electronics Society, 1998. IECON'98. Proceedings of the 24th Annual Conference of the IEEE, volume 4, pages 2374-2377. IEEE.
  7. Drews, P., Frassek, B., and Willms, K. (1986). Optical sensor systems for automated arc welding. Robotics, 2(1):31-43.
  8. Galamhos, C., Matas, J., and Kittler, J. (1999). Progressive probabilistic hough transform for line detection. In Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on., volume 1, pages -560 Vol. 1.
  9. Halmøy, E. (1999). Simulation of rotational arc sensing in gas metal arc welding. Science and Technology of Welding & Joining, 4(6):347-351.
  10. Hollitt, C. (2009). Reduction of computational complexity of hough transforms using a convolution approach. In Image and Vision Computing New Zealand, 2009. IVCNZ'09. 24th International Conference, pages 373-378. IEEE.
  11. Hou, X. and Liu, H. (2012). Welding image edge detection and identification research based on canny operator. In Computer Science & Service System (CSSS), 2012 International Conference on, pages 250-253. IEEE.
  12. Kawahara, M. (1983). Tracking control system using image sensor for arc welding. Automatica, 19(4):357-363.
  13. Kim, C. H. and Na, S. J. (2000). A study on rotating arc using hollow shaft motor. Journal of Korean Welding Society, 18:589-594.
  14. Liu, X. (2010). Image processing in weld seam tracking with laser vision based on radon transform and fcm clustering segmentation. ICICTA, pages 470-473.
  15. Ma, H., Wei, S., Sheng, Z., Lin, T., and Chen, S. (2010). Robot welding seam tracking method based on passive vision for thin plate closed-gap butt welding. The International Journal of Advanced Manufacturing Technology, 48(9-12):945-953.
  16. Nieto, M., Cuevas, C., Salgado, L., and García, N. (2011). Line segment detection using weighted mean shift procedures on a 2d slice sampling strategy. Pattern Analysis and Applications, 14(2):149-163.
  17. Perreault, S. and Hébert, P. (2007). Median filtering in constant time. Image Processing, IEEE Transactions on, 16(9):2389-2394.
  18. Risse, T. (1989). Hough transform for line recognition: complexity of evidence accumulation and cluster detection. Computer Vision, Graphics, and Image Processing, 46(3):327-345.
  19. Shirmohammadi, S. and Ferrero, A. (2014). Camera as the instrument: the rising trend of vision based measurement. Instrumentation & Measurement Magazine, IEEE, 17(3):41-47.
  20. Topal, C., Akinlar, C., and Genc¸, Y. (2010). Edge drawing: a heuristic approach to robust real-time edge detection. In Pattern Recognition (ICPR), 2010 20th International Conference on, pages 2424-2427. IEEE.
  21. Von Gioi, R. G., Jakubowicz, J., Morel, J.-M., and Randall, G. (2012). Lsd: a line segment detector. Image Processing On Line, 2(3):5.
  22. Xu, L., Lu, C., Xu, Y., and Jia, J. (2011). Image smoothing via l 0 gradient minimization. In ACM Transactions on Graphics (TOG), volume 30, page 174. ACM.
  23. Xu, Y., Yu, H., Zhong, J., Lin, T., and Chen, S. (2012). Real-time seam tracking control technology during welding robot gtaw process based on passive vision sensor. Journal of Materials Processing Technology, 212(8):1654-1662.
  24. Zhang, L., Ke, W., Ye, Q., and Jiao, J. (2014). A novel laser vision sensor for weld line detection on wall-climbing robot. Optics & Laser Technology, 60:69-79.
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Paper Citation


in Harvard Style

Steffens C., Leonardo B., Filho S., Huttner V., Rosa V. and Botelho S. (2016). Welding Groove Mapping - Implementation and Evaluation of Image Processing Algorithms on Shiny Surfaces . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 324-332. DOI: 10.5220/0005709103240332


in Bibtex Style

@conference{visapp16,
author={Cristiano Rafael Steffens and Bruno Quaresma Leonardo and Sidnei Carlos da Silva Filho and Valquiria Huttner and Vagner Santos da Rosa and Silvia Silva da Costa Botelho},
title={Welding Groove Mapping - Implementation and Evaluation of Image Processing Algorithms on Shiny Surfaces},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)},
year={2016},
pages={324-332},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005709103240332},
isbn={978-989-758-175-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2016)
TI - Welding Groove Mapping - Implementation and Evaluation of Image Processing Algorithms on Shiny Surfaces
SN - 978-989-758-175-5
AU - Steffens C.
AU - Leonardo B.
AU - Filho S.
AU - Huttner V.
AU - Rosa V.
AU - Botelho S.
PY - 2016
SP - 324
EP - 332
DO - 10.5220/0005709103240332