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A Model of Automatic Detection System for Weld Defects Based on Machine Vision

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Robotic Welding, Intelligence and Automation

Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 362))

Abstract

To realize weld defects detection and measure automation based on machine vision, such as dimension, shape, displacement and numbers in a given length (area), it’s necessary to build a mathematic model for the imaging system. Precision of model directly affects precision of measurement for defects. In this paper, a model was built including some nonlinear factors, i.e. the input screen shape (spherical surface) of the x-ray image intensifier and the distortion of the image in CCD at small distance. A kind of reason why the electromagnetic distortion generates is given in this paper, too

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References

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Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, P.R. China

    Yuan Tian, dong Du, runshi Hou, li Wang & guorui Cai

  2. Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, P.R. China

    Yuan Tian, dong Du, runshi Hou, li Wang & guorui Cai

Authors
  1. Yuan Tian
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  2. dong Du
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  3. runshi Hou
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  4. li Wang
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  5. guorui Cai
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Editor information

Editors and Affiliations

  1. Washington University, Cupples 2, Room 103, Campus Box 1040, St. Louis, MS, 63130, USA

    Tzyh-Jong Tarn

  2. Institute of Welding Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P.R. China

    Shan-Ben Chen

  3. Advanced Robotics and Intelligent Control Centre (ARICC), School of Electrical and Electronic Engineering, Singapore Polytechnic, 500 Dover Road, Singapore, 139651, Singapore

    Changjiu Zhou

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© 2007 Springer-Verlag Berlin Heidelberg

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Tian, Y., Du, d., Hou, r., Wang, l., Cai, g. (2007). A Model of Automatic Detection System for Weld Defects Based on Machine Vision. In: Tarn, TJ., Chen, SB., Zhou, C. (eds) Robotic Welding, Intelligence and Automation. Lecture Notes in Control and Information Sciences, vol 362. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73374-4_41

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  • DOI: https://doi.org/10.1007/978-3-540-73374-4_41

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73373-7

  • Online ISBN: 978-3-540-73374-4

  • eBook Packages: EngineeringEngineering (R0)

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