Recognition of Weld Penetration During K-TIG Welding Based on Acoustic and Visual Sensing


In the field of welding process control, on-line monitoring of welding quality based on multi-sensor information fusion has attracted more attention. In order to recognize the penetration state of the Keyhole mode Tungsten Inert Gas welded joint in real time, an acoustic and visual sensing system was established in this paper. The acoustic and visual features that characterize the penetration state of the welded joints in 34 dimensions were extracted and the variation of the acoustic signal and the keyhole geometry were analyzed. In addition, the weighted scoring criterion based on the Fisher distance and the maximum information coefficient (Fisher–MIC) and Support Vector Machine (SVM) model based on cross-validation (CV) are designed as the feature selection method. The feature selection method can evaluate the penetration recognition accuracy of different feature subsets. The experiment results show that the maximum recognition accuracy was 97.1655%, which was performed by the 10-dimension optimal feature subset and the CV–SVM model with particle swarm optimization (PSO–CV–SVM). It is proved that the selected acoustic and visual features can well characterize the penetration state of the welded joints, and the feature selection method and PSO–CV–SVM model have superior performance.

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  1. 1.

    Zhang, S. B., & Zhang, Y. M. (2001). Efflux plasma charge-based sensing and control of joint penetration during keyhole plasma arc welding. Welding Journal, 80(7), 157s–162s.

    Google Scholar 

  2. 2.

    Luo, M., & Shin, Y. C. (2015). Estimation of keyhole geometry and prediction of welding defects during laser welding based on a vision system and a radial basis function neural network. International Journal of Advanced Manufacturing Technology, 81(1–4), 263–276.

    Article  Google Scholar 

  3. 3.

    Cui, S. L., Liu, Z. M., Fang, Y. X., Luo, Z., Manladan, S. M., & Yi, S. (2017). Keyhole process in K-TIG welding on 4 mm thick 304 stainless steel. Journal of Materials Processing Technology, 243, 217–228.

    Article  Google Scholar 

  4. 4.

    Liu, Z., Wu, C. S., & Gao, J. (2013). Vision-based observation of keyhole geometry in plasma arc welding. International Journal of Thermal Sciences, 63(63), 38–45.

    Article  Google Scholar 

  5. 5.

    Han, G. M., Yun, S. H., Cao, X. H., & Li, J. Y. (2004). Acquisition and pattern recognition of spectrum information of welding metal transfer. Chinese Journal of Mechanical Engineering, 24(3), 699–703.

    Google Scholar 

  6. 6.

    Zhang, Z., Chen, H., Xu, Y., Zhong, J., Lv, N., & Chen, S. (2015). Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding. Mechanical Systems and Signal Processing, 60–61, 151–165.

    Article  Google Scholar 

  7. 7.

    Lv, N., Xu, Y., Zhang, Z., Wang, J., Chen, B., & Chen, S. (2013). Audio sensing and modeling of arc dynamic characteristic during pulsed Al alloy GTAW process. Sensor Review, 33(2), 141–156.

    Article  Google Scholar 

  8. 8.

    Pal, K., Bhattacharya, S., & Pal, S. K. (2010). Investigation on arc sound and metal transfer modes for on-line monitoring in pulsed gas metal arc welding. Journal of Materials Processing Tech, 210(10), 1397–1410.

    Article  Google Scholar 

  9. 9.

    Čudina, M., Prezelj, J., & Polajnar, I. (2008). Use of audible sound for on-line monitoring of gas metal arc welding process. Metalurgija, 47(2), 81–85.

    Google Scholar 

  10. 10.

    Tarn, J., & Huissoon, J. (2005). Developing psycho-acoustic experiments in gas metal arc welding. In IEEE International Conference on Mechatronics and Automation, 2005 (pp. 1112–1117 Vol. 1112).

  11. 11.

    Feng, Y., Luo, Z., Liu, Z., Li, Y., Luo, Y., & Huang, Y. (2015). Keyhole gas tungsten arc welding of AISI 316L stainless steel. Materials and Design, 85, 24–31.

    Article  Google Scholar 

  12. 12.

    Liu, Z. M., Fang, Y. X., Cui, S. L., Yi, S., Qiu, J. Y., Jiang, Q., et al. (2017). Keyhole thermal behavior in GTAW welding process. International Journal of Thermal Sciences, 114, 352–362.

    Article  Google Scholar 

  13. 13.

    Cui, S., Shi, Y., Sun, K., & Gu, S. (2017). Microstructure evolution and mechanical properties of keyhole deep penetration TIG welds of S32101 duplex stainless steel. Materials Science and Engineering A, 709, 214–222.

    Article  Google Scholar 

  14. 14.

    Ma, J., Susca, S., Bajracharya, M., Matthies, L., Malchano, M., & Wooden, D. (2012). Robust multi-sensor, day/night 6-DOF pose estimation for a dynamic legged vehicle in GPS-denied environments. In IEEE International Conference on Robotics and Automation (pp. 619–626).

  15. 15.

    Sattar, F., Karray, F., Kamel, M., Nassar, L., & Golestan, K. (2016). Recent advances on context-awareness and data/information fusion in ITS. International Journal of Intelligent Transportation Systems Research, 14(1), 1–19.

    Article  Google Scholar 

  16. 16.

    Sung, W. T. (2010). Multi-sensors data fusion system for wireless sensors networks of factory monitoring via BPN technology. Expert Systems with Applications, 37(3), 2124–2131.

    Article  Google Scholar 

  17. 17.

    Chen, B., Wang, J., & Chen, S. (2010). Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion. International Journal of Advanced Manufacturing Technology, 48(1–4), 83–94.

    Article  Google Scholar 

  18. 18.

    Zhang, Z., & Chen, S. (2017). Real-time seam penetration identification in arc welding based on fusion of sound, voltage and spectrum signals. Journal of Intelligent Manufacturing, 28(1), 207–218.

    Article  Google Scholar 

  19. 19.

    Chen, B., & Chen, S. (2010). A study on applications of multi-sensor information fusion in pulsed-GTAW. Industrial Robot, 37(2), 168–176.

    MathSciNet  Article  Google Scholar 

  20. 20.

    Lee, S. S., Kim, T. H., Hu, S. J., Cai, W. W., Li, J., & Abell, J. A. (2012). Characterization of joint quality in ultrasonic welding of battery tabs. Journal of Manufacturing Science and Engineering, 135(2), 2186–2199.

    Google Scholar 

  21. 21.

    Wang, J. F., Chen, B., Chen, H. B., & Chen, S. B. (2009). Analysis of arc sound characteristics for gas tungsten argon welding. Sensor Review, 29(3), 240–249.

    Article  Google Scholar 

  22. 22.

    Tam, J. (2008). Methods of characterizing gas-metal arc welding acoustics for process automation. Waterloo: University of Waterloo.

    Google Scholar 

  23. 23.

    Liu, Z. M., Fang, Y. X., Cui, S. L., Luo, Z., Liu, W. D., Liu, Z. Y., et al. (2016). Stable keyhole welding process with K-TIG. Journal of Materials Processing Technology, 238, 65–72.

    Article  Google Scholar 

  24. 24.

    Wu, D., Huang, Y., Chen, H., He, Y., & Chen, S. (2017). VPPAW penetration monitoring based on fusion of visual and acoustic signals using t-SNE and DBN model. Materials and Design, 123, 1–14.

    Article  Google Scholar 

  25. 25.

    Wang, J. F., Yu, H. D., Qian, Y. Z., Yang, R. Z., & Chen, S. B. (2011). Feature extraction in welding penetration monitoring with arc sound signals. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 225(9), 1683–1691.

    Article  Google Scholar 

  26. 26.

    Zhang, Y. M., & Zhang, S. B. (1999). Observation of the keyhole during plasma arc welding. Welding Journal, 78(2), 53S–58S.

    Google Scholar 

  27. 27.

    Reshef, D. N., Reshef, Y. A., Finucane, H. K., Grossman, S. R., Mcvean, G., Turnbaugh, P. J., et al. (2011). Detecting novel associations in large data sets. Science, 334(6062), 1518.

    Article  Google Scholar 

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This project was finically supported by the Science and Technology Planning Project of Guangdong Province (Grant No. 2015B010919005), Science and Technology Planning Project of Guangzhou City (Grant No. 201604046026), and National Natural Science Foundation of China (Grant No. 51374111).

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Correspondence to Yonghua Shi.

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Zhu, T., Shi, Y., Cui, S. et al. Recognition of Weld Penetration During K-TIG Welding Based on Acoustic and Visual Sensing. Sens Imaging 20, 3 (2019).

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  • K-TIG welding
  • Weld penetration
  • Acoustic sensing
  • Visual sensing
  • Feature selection