Skip to main content
SpringerLink
Log in

Image Selection Based on Grayscale Features in Robotic Welding

  • Conference paper
Artificial Intelligence and Computational Intelligence (AICI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7004))

Abstract

In robotic welding seam tracking based on visual information has been studied in the recent years. However, it is difficult to ensure the quality of images obtained in the welding process because it is easily affected by spattering, fuming and electromagnetic noise. The paper introduces a method to select useful images before further processing. Experimental tests are conducted to verify its accuracy.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Kim, J.W., Na, S.J.: Study on arc sensor algorithm for weld seam tracking in gas metal arc welding of butt joints. In: Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 205(B4), pp. 247–255 (1991)

    Google Scholar 

  2. Suga, Y., Naruse, M., Tokiwa, T.: Application of neural network to visual sensing of weld line and automatic tracking in robot welding. Welding in the World 34, 275–282 (1994)

    Google Scholar 

  3. Kuo, H.-C., Wu, L.-J.: An image tracking system for welded seams using fuzzy logic. J. Mater. Process. Technol. 120(1), 169–185 (2000)

    MathSciNet  Google Scholar 

  4. Lee, S.K., Na, S.J.: A study on automatic seam tracking in pulsed laser edge welding by using a vision sensor without an auxiliary light source. J. Manuf. Syst. 21(4), 302–315 (2002)

    Article  Google Scholar 

  5. Lee, S.K., Chang, W.S., Yoo, W.S., Na, S.J.: A tudy on a vision sensor based laser welding system for bellows. J. Manuf. Syst. 19(4), 249–255 (2007)

    Article  Google Scholar 

  6. Micallef, K., Fang, G., Dinham, M.: Automatic Seam Detection and Path Planning in Robotic Welding. In: Tarn, T.J., Chen, S.B., Fang, G. (eds.) Robotic Welding, Intelligence and Automation, pp. 23–32. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  7. Bae, K.Y., Lee, T.H., Ahn, K.C.: An optical sensing system for seam tracking and weld pool control in gas metal arc welding of steel pipe. J. Mater. Process. Technol. 120(2), 458–465 (2002)

    Article  Google Scholar 

  8. Shen, H.Y., Li, L.P., Lin, T., Chen, S.B.: Real-time Seam Tracking Technology of Welding Robot with Visual Sensing. Journal of Intelligent and Robotic systems 59, 283–298 (2010)

    Article  MATH  Google Scholar 

  9. Ma, H., Wei, S., Sheng, Z.: Robot welding seam tracking method based on passive vision for thin plate closed-gap butt welding. International Journal of Advanced Manufacturing Technology 48, 945–953 (2010)

    Article  Google Scholar 

  10. Yan, Z.H., Zhang, G.J., Gao, H.M., Wu, L.: Weld pool boundary and weld bead shape reconstruction based on passive vision in P-GMAW. China Welding 15(2), 20–24 (2006)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of engineering, University of Western Sydney, 2751, Australia

    Zhen Ye, Gu Fang & Ju Jia Zou

  2. Institute of Welding Engineering, Shanghai Jiao Tong University, 200240, China

    Zhen Ye & Shanben Chen

Authors
  1. Zhen Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gu Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shanben Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ju Jia Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Business Information Technology, RMIT University, City Campus, 124 La Trobe Street, 3000, Melbourne, VIC, Australia

    Hepu Deng

  2. School of Electronics and Information, Tongji University, 201804, Shanghai, China

    Duoqian Miao

  3. School of Computer and Information Engineering, Shanghai University of Electric Power, 200090, Shanghai, China

    Jingsheng Lei

  4. Department of Business Administration, Caritas Institute of Higher Education, 18 Chui Ling Road, Tseung Kwan O, Hong Kong, China

    Fu Lee Wang

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ye, Z., Fang, G., Chen, S., Zou, J.J. (2011). Image Selection Based on Grayscale Features in Robotic Welding. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7004. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23896-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-23896-3_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23895-6

  • Online ISBN: 978-3-642-23896-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation