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Real-time seam penetration identification in arc welding based on fusion of sound, voltage and spectrum signals

Author

Listed:
  • Zhifen Zhang

    (Shanghai Jiao Tong University)

  • Shanben Chen

    (Shanghai Jiao Tong University)

Abstract

Sensor technology application is the key for intelligent welding process. Multiple sensors fusion has shown their significant advantages over single sensor which can only provide limited information. In this paper, a feature-level data fusion methodology was presented to automatically evaluate seam quality in real time for Al alloy in gas tungsten arc welding by means of online arc sound, voltage and spectrum signals. Based on the developed algorithms in time and frequency domain, multiple feature parameters were successively extracted and selected from sound and voltage signals, while spectrum distribution of argon atoms related to seam penetration were carefully analyzed before feature parameters selection. After the synchronization of heterogeneous feature parameters, the feature-level-based data fusion was conducted by establishing a classifier using support vector machine and 10-fold cross validation. The test results indicate that multisensory-based classifier has higher accuracy i.e., 96.5873 %, than single sensor-based one in term of recognizing seam defects, like under penetration and burn through from normal penetration.

Suggested Citation

  • Zhifen Zhang & Shanben Chen, 2017. "Real-time seam penetration identification in arc welding based on fusion of sound, voltage and spectrum signals," Journal of Intelligent Manufacturing, Springer, vol. 28(1), pages 207-218, January.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:1:d:10.1007_s10845-014-0971-y
    DOI: 10.1007/s10845-014-0971-y
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    Cited by:

    1. Wanyou Lv & Jiawen Xiong & Jianqi Shi & Yanhong Huang & Shengchao Qin, 2021. "A deep convolution generative adversarial networks based fuzzing framework for industry control protocols," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 441-457, February.
    2. Abdallah Amine Melakhsou & Mireille Batton-Hubert, 2023. "Welding monitoring and defect detection using probability density distribution and functional nonparametric kernel classifier," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1469-1481, March.
    3. Runquan Xiao & Yanling Xu & Zhen Hou & Chao Chen & Shanben Chen, 2022. "An automatic calibration algorithm for laser vision sensor in robotic autonomous welding system," Journal of Intelligent Manufacturing, Springer, vol. 33(5), pages 1419-1432, June.

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