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Multi-sensor information fusion for monitoring disk laser welding

Abstract

This paper describes a multi-sensor fusion system for monitoring disk laser welding process. During bead-on-plate disk laser welding of type 304 austenitic stainless steel plates, a multi-sensor fusion system was applied to monitor the welding process, which consisted of auxiliary illumination (AI) sensing, ultraviolet and visible (UVV) sensing, and photodiode sensing. The visual sensing based on auxiliary illumination was used to capture the dynamic behavior of a molten pool and keyhole. The ultraviolet and visible sensing was to capture the dynamic behavior of plume and spatter. Photodiode sensing was to monitor the visible light emission and laser reflection. The features that were extracted from the sensing signals were used for monitoring disk laser welding based on a backpropagation (BP) neural network. Experimental results showed that the integration of photodiode and visual sensing provided a more accurate estimation on the laser welding process.

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References

  1. 1.

    Shao Y, Wang ZZ, Zhang YM (2011) Monitoring of liquid droplets in laser-enhanced GMAW. Int J Adv Manuf Technol 57:203–214

  2. 2.

    Riahi M, Amini A (2013) Effect of different combinations of tailor-welded blank coupled with change in weld location on mechanical properties by laser welding. Int J Adv Manuf Technol 67:1937–1945

  3. 3.

    Huang Y, Xiao YL, Wang PJ, Li MZ (2013) A seam-tracking laser welding platform with 3D and 2D visual information fusion vision sensor system. Int J Adv Manuf Technol 67:415–426

  4. 4.

    Gao XD, Chen YQ (2014) Detection of micro gap weld using magneto-optical imaging during laser welding. Int J Adv Manuf Technol 73:23–33

  5. 5.

    Zhang Y, Zhang CL, Tan LP, Li SC (2013) Coaxial monitoring of the fiber laser lap welding of Zn-coated steel sheets using an auxiliary illuminant. Opt Laser Technol 50:167–175

  6. 6.

    Huang W, Kovacevic R (2012) Development of a real-time laser-based machine vision system to monitor and control welding processes. Int J Adv Manuf Technol 63(1–4):235–248

  7. 7.

    Sibillano T, Ancona A, Berardi V, Lugara PM (2007) Real-time monitoring of laser welding by correlation analysis: the case of AA5083. Opt Laser Eng 45:1005–1009

  8. 8.

    Zhang YX, Gao XD (2014) Analysis of characteristics of molten pool using cast shadow during high-power disk laser welding. Int J Adv Manuf Technol 70:1979–1988

  9. 9.

    Zhang W, Hua XM, Liao W, Li F, Wang M (2014) Behavior of the plasma characteristic and droplet transfer in CO2 laser-GMAW-P hybrid welding. Int J Adv Manuf Technol 72:935–942

  10. 10.

    Liu W, Liu S, Ma JJ, Kovacevic R (2014) Real-time monitoring of the laser hot-wire welding process. Opt Laser Technol 57:66–76

  11. 11.

    Gang T, Shi DH, Yuan Y, Yang SY (2006) Segmentation of small defects in laser weld of titanium alloy with complex structure. Insight 48(12):731–734

  12. 12.

    Honda H, Tsukamoto S, Kawaguchi I, Arakane G (2010) Keyhole behavior in deep penetration CO2 laser welding. J Laser Appl 22(2):43–47

  13. 13.

    Gao XD, Sun Y (2014) Monitoring of high-power disk laser welding of type 304 austenitic stainless steel based on keyhole dynamic characteristics. Insight 56(6):312–317

  14. 14.

    Gao XD, Sun Y, Katayama S (2014) Neural network of plume and spatter for monitoring high-power disk laser welding. Int J Precis Eng Manuf-Green Technol 22(2):43–47

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Correspondence to Xiangdong Gao.

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Gao, X., Sun, Y., You, D. et al. Multi-sensor information fusion for monitoring disk laser welding. Int J Adv Manuf Technol 85, 1167–1175 (2016). https://doi.org/10.1007/s00170-015-8032-z

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Keywords

  • Multi-sensor information fusion
  • Ultraviolet and visible sensing
  • Auxiliary illumination sensing
  • Photodiode sensing