Vision Based Identification and Detection of Initial, Mid and End Points of Weld Seams Path in Butt-Welding Joint using Point Detector Methods

Authors

  • H. N. Mohd Shah Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia.
  • M. Sulaiman Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia.
  • A. Z. Shukor Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia.
  • M. Z. Ab Rashid Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian Tunggal, Melaka, Malaysia.

Keywords:

Harris Binomial, Harris, Lepetit, Sojka and Weld Seams Path,

Abstract

The main challenge in using welding robot is the time taken to program robot path for a new job in low to medium volume manufacturing industries or repair work. Because of that, it is cheaper and efficient to weld the part manually. This project intends to identify and detect the initial, mid and end point of weld seams path in straight line joints of stainless steel piecework which are typical to welding applications. The image piecework is snapped by charge coupled device (CCD) camera which is perpendicular between piecework. Weld seams path identification method is implemented in three stages; (1) preprocessing (2) reduced domain and (3) the weld seams path is identified. The point detection techniques is used to find the point of the images. Point detection techniques such as Harris Binominal, Harris, Lepetit and Sojka points were analyed to remove those points that does not belong to seam path. The experimental results show this systems can identify and detect weld seam path location in terms of x-y pixels coordinates. Result show that, Harris points detector is the best point detector compared to the other detectors which the identification error is around ±2.0 pixels for both coordinates which is row and column in starting and ending points of weld seams path.

References

Kong M, Shi FH, Chen SB, Lin T. Recognition of the initial position of weld based on the corner identification for welding robot in global environment. In: Tarn TJ, et al., editors. Robotic welding intelligence and automation, LNCIS, 362. Berlin Heidelberg: Springer Verlag; 2007. pp 249–55.

Micallef K, Fang G, Dinham M. Automatic seam identification and path planning in robotic welding. In: Tarn TJ, Chen SB, Fang G, editors. Robotic welding intelligence and automation, LNEE, 88. Berlin Heidelberg: Springer Verlag; 2011. Pp. 23–32.

Pachidis TP, Lygouras JN. Vision-based path generation method for a robot based arc welding system. Journal of Intelligent Robot Systems 2007;48(3): pp. 307–331.

Kong, M. et al “Recognition of the Initial Position of Weld Based on the Corner Detection for Welding Robot in Global Environment” in Robotic Welding Intelligence & Automation, LNCIS, (Eds. T.J. Tarn et al), Springer Verlag Berlin Heidelberg, 2007, 362, pp. 249-255.

Micallef, K., Fang, G., Dinham, M., “Automatic Seam Detection and Path Planning in Robotic Welding” in Robotic Welding Intelligence & Automation, LNEE, (Eds. T.J. Tarn et al), Springer Verlag Berlin Heidelberg, 2011, 88, pp. 23-32

Pachidis, T.P.,Lygouras, J.N., “Vision-Based Path Generation Method for a Robot Based Arc Welding System” in Journal of Intelligent Robot Systems, 2007, 48(3), pp. 307-331.

Chen, X. Z., & Chen, S. B. (2010). The autonomous identification and guiding of start position for arc welding robot. Industrial Robot: An International Journal, 37(1), 70-78.

Ye, Z., Fang, G., Chen, S., & Dinham, M. (2013). A robust algorithm for weld seam extraction based on prior knowledge of weld seam. Sensor, 33, pp. 125-133. for weld seam extraction based on prior knowledge of weld seam. Sensor, 33, pp. 125-133.

Dinham, M., & Fang, G. (2012). Weld seam detection using computer vision for robotic arc welding. In: Proceedings of the 2012 IEEE international conference on automation science and engineering, pp. 679-774.

Chang, D. Y., Son, D. H., Lee, J. W., Lee, D. H., Kim, T. W., Lee, K. Y., & Kim, J. (2012). A new seam-tracking algorithm through characteristicpoint detection for a portable welding robot. ournal Robotics and Computer-Integrated Manufacturing, 28, pp. 1-13.

Sulaiman, M., Shah, M, H. N., Harun, M. H., Lim., W. T.,& Kazim, M., N. F. M (2013). A 3D Gluing Defect Inspection System Using ShapeBased Matching Application from Two Cameras. International Review on Computers and Software (IRECOS), 8(8), pp. 1997-2004.

Sulaiman, M., Shah, M, H. N., Harun, M. H., & Kazim, M., N. F..

(2014). Defect Inspection System For Shape-Based Matching Using Two Cameras. Journal of Theoretical and Applied Information Technology (JATIT), 61(2), pp. 288-297.

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Published

2016-10-01

How to Cite

Mohd Shah, H. N., Sulaiman, M., Shukor, A. Z., & Ab Rashid, M. Z. (2016). Vision Based Identification and Detection of Initial, Mid and End Points of Weld Seams Path in Butt-Welding Joint using Point Detector Methods. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 8(7), 57–61. Retrieved from https://jtec.utem.edu.my/jtec/article/view/1279