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Transactions on Intelligent Welding Manufacturing pp 87–104Cite as

Online Monitoring of Variable Polarity TIG Welding Penetration State Based on Fusion of Welding Characteristic Parameters and SVM

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Part of the book series: Transactions on Intelligent Welding Manufacturing ((TRINWM))

Abstract

In variable polarity TIG (VPTIG) welding of aluminum alloy, effective recognition of welding penetration states is a hot research topic. It is also one of the key factors for the quality of weld and the joint represent. We established an intelligent sensor system for VPTIG welding to obtain the welding current, misalignment and interval, the clear weld pool images and wire feed speed online. With an effective image processing algorithm, weld pool width is measured accurately online. To investigate the complicated relationships between the welding parameter and different welding condition, an improved Support Vector Machines (SVM) classification model based on artificial fish swarm algorithm is built. The work shows that the proposed Support Vector Machine model classifies aluminum alloy welding states effectively.

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References

  1. Shen HY et al (2015). Research on weld pool control of welding robot with computer vision. industrial robot. In: Conference on industrial engineering and management innovation, vol 34, Springer, Heidelberg, pp 275–285

    Google Scholar 

  2. Zhang ZF, Chen HB, Zhong JY et al (2015) Multisensor-based real-time quality monitoring by means of feature extraction. Mech Syst Signal Process 60(61):151–165

    Article  Google Scholar 

  3. Chen HB, Lv FL, Lin T et al (2009) Closed-loop control of robotic arc welding system with full-penetration monitoring. J Intell Robot Syst 56(3):565–578

    Article  Google Scholar 

  4. Lv N, Xu Y, Zhang Z et al (2013) Audio sensing and modeling of arc dynamic characteristic during pulsed Al alloy GTAW process. Sens Rev 32(21):375–385

    Google Scholar 

  5. Fan CJ, Chen SB, Lin T (2007) Visual sensing and image processing in aluminum alloy welding. Lect Notes Control Inf Sci 362(30):275–280

    Google Scholar 

  6. Chen B, Chen SB (2009) Prediction of pulsed GTAW status based on fuzzy integral information fusion. Assembly Autom 56(6):100–108

    Google Scholar 

  7. Chen B, Wang JF, Chen SB (2010) Prediction of pulsed GTAW penetration status based on BP neural network and D-S evidence theory information fusion. Int J Adv Manufact 87(4):83–94

    Article  Google Scholar 

  8. Bi SJ, Lan H, Liu LJ (2010) MAG welding penetration status online monitoring based on the analysis of arc sound signal characteristics. J Weld 31(2):17–20

    Google Scholar 

  9. Wang CM, Wu SP, Hu LJ et al (2007) Identification of different laser welding penetration states based on multi-sensor fusion. Chin J Lasers 34(65):538–542

    Google Scholar 

  10. Zhang SQ, Hu SS, Wang ZJ (2016) Weld penetration sensing in pulsed gas tungsten arc welding based on arc voltage. Chinese J Mater Process Technol 52(60):520–527

    Article  Google Scholar 

  11. Huang XX, Chen SB (2006) SVM-based fuzzy modeling for the arc welding process. Mater Sci Eng, A 427(1–2):181–187

    Google Scholar 

  12. Chen B, Wang JF, Chen SB (2010) A study on applications of multi-sensor fusion in pulsed GTAW. Ind Robot 37(67):168–176

    Article  Google Scholar 

  13. Wang JF, Chen HB, Chen SB (2009) Analysis of arc sound characteristics for gas tungsten argon welding. Sens Rev 29(54):240–249

    Article  Google Scholar 

  14. Lin T, Chen HB, Li WH et al (2009) Intelligent methodology for sensing, modeling and control of weld penetration in robotic welding system. Ind Robot 36(68):583–593

    Google Scholar 

  15. Cheng CY et al (2016) Hybrid artificial fish algorithm to solve TSP problem. In: Proceedings of the 6th international Asia conference on industrial engineering and management innovation, vol 8, Atlantis Press, Heidelberg, pp 1246–1255

    Google Scholar 

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Author information

Authors and Affiliations

  1. Intelligentized Robotic Welding Technology Laboratory, Shanghai Jiao Tong University, Shanghai, 200240, China

    Liang Liu, Huabin Chen & Shanben Chen

Authors
  1. Liang Liu
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  2. Huabin Chen
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  3. Shanben Chen
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Corresponding author

Correspondence to Huabin Chen .

Editor information

Editors and Affiliations

  1. Shanghai Jiao Tong University , Shanghai, China

    Shanben Chen

  2. Department of Electrical and Computer Engineering, University of Kentucky, Lexington, Kentucky, USA

    Yuming Zhang

  3. Oak Ridge National Laboratory, Oak Ridge, Tennessee, USA

    Zhili Feng

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© 2019 Springer Nature Singapore Pte Ltd.

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Cite this paper

Liu, L., Chen, H., Chen, S. (2019). Online Monitoring of Variable Polarity TIG Welding Penetration State Based on Fusion of Welding Characteristic Parameters and SVM. In: Chen, S., Zhang, Y., Feng, Z. (eds) Transactions on Intelligent Welding Manufacturing. Transactions on Intelligent Welding Manufacturing. Springer, Singapore. https://doi.org/10.1007/978-981-10-8740-0_5

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