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Automatic extraction and identification of narrow butt joint based on ANFIS before GMAW

  • Lei Yang
  • En LiEmail author
  • Junfeng Fan
  • Teng Long
  • Zize Liang
ORIGINAL ARTICLE
  • 23 Downloads

Abstract

To improve the automation level of the welding robots, the automatic extraction and identification of the weld seam is the pre-requisite of the intelligent welding robots. During the real industrial environment, the welding environment often has the characteristics of weak texture, poor contrast, reflections from metallic surfaces, and imperfections on the work piece. These characteristics will seriously affect the accurate extraction of weld seam. To realize the automatic path planning of welding robots, a new method of automatic extraction and identification of narrow butt joint under weak contrast is realized to serve the 3D path teaching of welding robots. To achieve accurate extraction of narrow butt joint under weak contrast, this paper designs a new seam extraction operator to achieve accurate and fast extraction of weld seam with various shapes and sizes. And the shape feature of weld seam is used to construct feature vector. Finally, to get optimal classification performance, the adaptive network-based fuzzy inference system (ANFIS) is adopted in this paper to finish identification of narrow butt joints. The experimental results show that the proposed algorithm could quickly and accurately realize the extraction and identification of the narrow butt joints under weak contrast.

Keywords

Narrow butt joint Weak contrast Extraction Identification ANFIS 

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Notes

Acknowledgements

The authors would like to thank the anonymous referees for their valuable suggestions and comments.

Funding information

This work was supported by the National Key Research and Development Program of China (Grant No. 2017YFD0701401) and the National Natural Science Foundation of China (Grant No. U1713224).

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Lei Yang
    • 1
    • 2
  • En Li
    • 1
    Email author
  • Junfeng Fan
    • 1
    • 2
  • Teng Long
    • 1
    • 2
  • Zize Liang
    • 1
  1. 1.The State Key Laboratory of Management and Control for Complex SystemsInstitute of Automation, Chinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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