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Weld penetration identification with deep learning method based on auditory spectrum images of arc sounds

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Abstract

Penetration states significantly affect the service performance of weld products. For improving welding quality, it is essential to real-timely monitor the penetration states of molten pool during welding process. This study adopts arc sound signals to identify penetration states of weld seam. Firstly, the time–frequency spectrum images of arc sounds are obtained with short-time Fourier transform. And based on a convolution neural network, the penetration states of weld seam are identified. For improving the anti-interference ability of the proposed identification method, a mathematical model that simulates the functions of human auditory system is developed. The auditory spectrum images of arc sounds are acquired with this model. Based on the auditory spectrum images of arc sounds, the penetration states are identified. The experimental results show that the proposed method has high anti-interference ability. When the signal-to-noise ratio is less than 5 dB, the accuracy rate of identification keeps more than 95%.

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References

  1. Xiao RQ, Xu YL, Hou Zh, Chen H, Chen SB (2019) An adaptive feature extraction algorithm for multiple typical seam tracking based on vision sensor in robotic arc welding. Sens Actuators A:Physical 297:111533. https://doi.org/10.1016/j.sna.2019.111533

    Article  CAS  Google Scholar 

  2. Yang D, Wang G, Zhang G (2017) A comparative study of GMAW- and DE-GMAW-based additive manufacturing techniques: thermal behavior of the deposition process for thin-walled parts. Int J Adv Manuf Technol 91:2175–2184. https://doi.org/10.1007/s00170-016-9898-0

    Article  Google Scholar 

  3. Huang J, Yang M, Chen J et al (2018) The oscillation of stationary weld pool surface in the GTA welding. J Mater Process Technol 256:57–68

    Article  Google Scholar 

  4. Song S, Chen H, Lin T et al (2016) Penetration state recognition based on the double-sound-sources characteristic of VPPAW and hidden Markov Model. J Mater Process Technol 234:33–44. https://doi.org/10.1016/j.jmatprotec.2016.03.002

    Article  CAS  Google Scholar 

  5. Zhang Z, Chen S (2017) Real-time seam penetration identification in arc welding based on fusion of sound, voltage and spectrum signals. J Intell Manuf 28:207–218. https://doi.org/10.1007/s10845-014-0971-y

    Article  Google Scholar 

  6. Lv N, Xu Y, Li S et al (2017) Automated control of welding penetration based on audio sensing technology. J Mater Process Technol 250:81–98. https://doi.org/10.1016/j.jmatprotec.2017.07.005

    Article  Google Scholar 

  7. Zhang Z, Wen G, Chen S (2018) Audible sound-based intelligent evaluation for aluminum alloy in robotic pulsed GTAW: mechanism, feature selection, and defect detection. IEEE Trans Ind Informatics 14:2973–2983. https://doi.org/10.1109/TII.2017.2775218

    Article  Google Scholar 

  8. Gao Y, Zhao J, Wang Q et al (2020) Weld bead penetration identification based on human-welder subjective assessment on welding arc sound. Meas J Int Meas Confed 154:107475. https://doi.org/10.1016/j.measurement.2020.107475

    Article  Google Scholar 

  9. Gao Y, Wang Q, Xiao J, Zhang H (2020) Penetration state identification of lap joints in gas tungsten arc welding process based on two channel arc sounds. J Mater Process Tech 285:116762. https://doi.org/10.1016/j.jmatprotec.2020.116762

    Article  CAS  Google Scholar 

  10. Haichao L, Liu J, Xie J, Wang X (2019) GTAW penetration prediction model based on convolution neural network algorithm. J Mech Eng 55:22–28

    Article  Google Scholar 

  11. Ren W, Wen G, Liu S et al. (2018) Seam penetration recognition for GTAW using convolutional neural network based on time-frequency image of arc sound. IEEE 23rd Int Conf Emerg Technol Fact Autom ETFA 2018-Sept, pp 853–860. https://doi.org/10.1109/ETFA.2018.8502478

  12. Wu D, Huang Y, Zhang P et al (2020) Visual-acoustic penetration recognition in variable polarity plasma arc welding process using hybrid deep learning approach. IEEE Access 8:120417–120428. https://doi.org/10.1109/ACCESS.2020.3005822

    Article  Google Scholar 

  13. Shamma SA (1985) Speech processing in the auditory system II: lateral inhibition and the central processing of speech evoked activity in the auditory nerve. J Acoust Soc Am 78:1622–1632. https://doi.org/10.1121/1.392800

    Article  CAS  Google Scholar 

  14. Patterson RD, Moore BCJ (1986) Auditory filters and excitation patterns as representations of auditory frequency selectivity. In: Moore BCJ (ed) Frequency Selectivity in Hearing. Academic Press, London, pp 123–177

  15. Hewitt MJ, Meddis R (1991) An evaluation of eight computer models of mammalian inner hair-cell function. J Acoust Soc Am 90:904–917. https://doi.org/10.1121/1.401957

    Article  CAS  Google Scholar 

  16. Wang K, Shamma SA (1995) Spectral shape analysis in the central auditory system. IEEE Trans Speech Audio Process 3:382–395. https://doi.org/10.1109/89.466657

    Article  Google Scholar 

  17. Martin KD (1999) Sound-Source Recognition: a Theory and Computational Model. Dissertation, Massachusetts Institute of Technology

  18. Patterson RD (2000) Auditory images : how complex sounds are represented in the auditory system. Acoust Sci Technol 21:183–190

    Google Scholar 

  19. ANSI S3.4-2007 (2007) Procedure for the computation of loudness of steady sounds. American National Standards Institute, Melville

  20. Greenwood DD (1990) A cochlear frequency-position function for several species—29 years later. J Acoust Soc Am 87:2592–2605

    Article  CAS  Google Scholar 

  21. Johannesma PIM (1988) Spectro-temporal interpretation of activity patterns of auditory neurons GERARD H. F M HESSELMANS 51:31–51

    Google Scholar 

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Funding

This work was supported by the Natural Science Foundation of Shanghai (21ZR1425900, 21010501600).

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Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

    Yanfeng Gao, Genliang Xiong & Hua Zhang

  2. Shanghai Collaborative Innovation Center of Intelligent Manufacturing Robot Technology for Large Components, Shanghai, 201620, China

    Yanfeng Gao, Genliang Xiong & Hua Zhang

  3. Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Huihong Building, Changwu Middle Road 801, Changzhou, 213164, Jiangsu, China

    Qisheng Wang

  4. Department of Science Island, University of Science and Technology of China, Anhui, 230026, Hefei, China

    Qisheng Wang

  5. School of Chemistry and Chemical Engineering, Shanghai University of Engineering Science, Shanghai, 201620, China

    Jianhua Xiao

Authors
  1. Yanfeng Gao
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  2. Qisheng Wang
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  3. Jianhua Xiao
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  4. Genliang Xiong
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  5. Hua Zhang
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Contributions

Yanfeng Gao: conceptualization, methodology, writing—original draft preparation, funding acquisition.

Qisheng Wang: investigation.

Jianhua Xiao: data curation, visualization.

Genliang Xiong: writing—review and editing.

Hua Zhang: supervision, project administration.

Corresponding authors

Correspondence to Yanfeng Gao or Qisheng Wang.

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Gao, Y., Wang, Q., Xiao, J. et al. Weld penetration identification with deep learning method based on auditory spectrum images of arc sounds. Weld World 66, 2509–2520 (2022). https://doi.org/10.1007/s40194-022-01373-7

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  • DOI: https://doi.org/10.1007/s40194-022-01373-7

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