A Study on Construction of Automatic Inspection System for Welding Flaws

용접결함 검사 자동화 시스템 구축에 관한 연구

  • 김창현 (전남대학교 공과대학 전자컴퓨터공학부) ;
  • 유홍연 (전남대학교 공과대학 전자컴퓨터공학부) ;
  • 홍성훈 (전남대학교 공과대학 전자컴퓨터공학부) ;
  • 김재열 (조선대학교 공과대학 메카트로닉스공학과)
  • Published : 2007.12.15

Abstract

The purpose of this research is stability estimation of plant structure through classification and recognition about welding flaw in SWP(Spiral Welding Pipe). And, In this research, we used nondestructive test based on ultrasonic test as inspection method, and made up 2-axes inspection robot in order to control of ultrasonic probe on the SWP surface, and programmed to image processing and probabilistic neural network(PNN) classifying code by MATLAB programming. Through this process, we proved efficiency on the system of SWP stability Estimation.

Keywords

References

  1. International Institute of Welding, 1987, The Evaluation of Ultrasonic Signals, Welding Institute for International Institute of Welding, Cambridge, England
  2. Rose, J. L., 1999, Ultrasonics Waves in Solid Media, Cambridge University press, England
  3. Zhu, W., Rose, J. L., Barshinger, J. N. and Agarwala, V. S., 1998, 'Ultrasonic guided wave NDT for hidden corrosion detection,' Research in Nondestructive Evaluation, Vol. 10, No. 4, pp. 205-225 https://doi.org/10.1080/09349849809409629
  4. Liu, L., Avioli, M. J. and Rose, J. L, 2001, 'Incident angle selection for the guided wave inspection of pipe defects,' Journal of Insight, Vol. 43, No. 2
  5. Hay, T. R., Luo, W., Rose, J. L. and Hayashi, T., 2003, 'Rapid Inspection of Composite Skin-Honeycomb Core Structures with Ultrasonic Guided Waves,' Journal of Composite materials, Vol. 37, pp. 929-939 https://doi.org/10.1177/0021998303037010005
  6. Yoon, S. U., Kim, C. H. and Kim, J. Y., 2006, 'The Performance Comparison of Classifier Algorithm for Pattern Recognition of Welding Flaws,' Transactions of the Korean Society of Machine Tool Engineers, Vol. 15, No. 3, pp. 39-44
  7. Vinay, K. I. and John, G. P., 1998, Digital Signal Processing, Sigma-press Pub., pp. 353-428
  8. Rutkowski, L., 2004, New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing, New York, Springer Verlag
  9. Lee, H. Y. and Moon, K. I., 1999, Fuzzy-Neuro using Matlab, A-Jin Pub., South Korea
  10. Song, S. J., 1999, 'Nondestructive Flaw Classification by Pattern Recognition Approach,' Journal of KSNT, Vol. 19, No. 5, pp. 378-391
  11. Ganchev, T., Fakotakis, N. and Kokkinakis, G., 2002, 'Text-Independandent Speaker Verification based on Probabilistic Neural Networks,' Proceedings of the Acoustics, pp. 159-166
  12. Rutkowski, L. and Cpalka, K, 2003, 'Flexible Neuro-Fuzzy System,' Transactions of IEEE : Neural Network, Vol. 14, pp. 554-574 https://doi.org/10.1109/TNN.2003.811698
  13. Park, I. K, Park, U. S., Kim, Y. W., Kang, S. C., Choi, T. H. and Lee, J. H., 2001, 'Models of Reliability Assessment of Ultrasonic Nondestuctive Inspection,' Journal of KSNT, Vol. 21, No. 6, pp. 607-611
  14. Yi, W. and Yun, I. S., 1998, 'A Study on defect Classification and Evaluation in Weld Zone of Austenite Stainless Steel 304 Using Neural Network,' Journal of KSPE, Vol. 15, No. 7, pp. 149-159