Sensing and Imaging

, 20:3 | Cite as

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

  • Tao Zhu
  • Yonghua ShiEmail author
  • Shuwan Cui
  • Yanxin Cui
Original Paper


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.


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



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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Tao Zhu
    • 1
    • 2
  • Yonghua Shi
    • 1
    • 2
    Email author
  • Shuwan Cui
    • 1
    • 2
  • Yanxin Cui
    • 1
    • 2
  1. 1.School of Mechanical and Automotive EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.Guangdong Provincial Engineering Research Center for Special Welding Technology and EquipmentSouth China University of TechnologyGuangzhouChina

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