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Defect intelligent identification in resistance spot welding ultrasonic detection based on wavelet packet and neural network

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Abstract

In this paper, ultrasonic echo signals of four kinds of stainless steel resistance spot welds, namely failed weld, stick weld, good weld, and defective weld with gas pore, are analyzed in the time domain, frequency domain, and time-frequency domain based on wavelet packet transform. Fourteen ultrasonic characteristic signals which can reflect the different kinds of spot welds are extracted and can be automatically identified and classified by back-propagation (BP) neural network. The method of this paper can realize the intelligent identification of resistance spot welding defects, and the feasibility of this method has been verified in the experiment.

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Authors and Affiliations

  1. Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun, 130025, People’s Republic of China

    Jing Liu, Lei Ren, Zhihui Qian & Luquan Ren

  2. Key Laboratory of Automobile Materials of Ministry of Education and Department of Materials Science & Engineering, Jilin University, Changchun, 130025, People’s Republic of China

    Jing Liu & Guocheng Xu

  3. School of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester, M13 9PL, UK

    Lei Ren

Authors
  1. Jing Liu
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  2. Guocheng Xu
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  3. Lei Ren
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  4. Zhihui Qian
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  5. Luquan Ren
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Correspondence to Lei Ren or Zhihui Qian.

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Liu, J., Xu, G., Ren, L. et al. Defect intelligent identification in resistance spot welding ultrasonic detection based on wavelet packet and neural network. Int J Adv Manuf Technol 90, 2581–2588 (2017). https://doi.org/10.1007/s00170-016-9588-y

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  • DOI: https://doi.org/10.1007/s00170-016-9588-y

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