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Modeling and robust controlling of laser welding process on high strength titanium alloy using fuzzy basis function networks and robust Takagi-Sugeno fuzzy controller

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Abstract

The purpose of this paper is to present and implement a novel approach to data-driven modeling and robust control of a laser keyhole welding process. The objective is to maintain the penetration depth of the laser welding process at a desired value under system uncertainties. A framework was proposed to estimate the keyhole diameter and the keyhole penetration depth in real time by type-1 and type-2 fuzzy basis function networks, and an adaptive divided difference filter. A robust Takagi-Sugeno fuzzy controller was implemented to adjust the laser power of the welding process. Experimental results demonstrated that the fuzzy models provided an accurate estimation of both the welding geometry and its variations due to uncertainties, and the robust Takagi-Sugeno fuzzy controller successfully reduced the penetration depth variation and improved the quality of the welding process.

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References

  1. Matsunawa A, Kim J-D, Seto N et al (1998) Dynamics of keyhole and molten pool in laser welding. J Laser Appl 10:247–254. doi:10.2351/1.521858

    Article  Google Scholar 

  2. Ki H, Mohanty PS, Mazumder J (2002) Modeling of laser keyhole welding: Part I. Mathematical modeling, numerical methodology, role of recoil pressure, multiple reflections, and free surface evolution. Metall Mater Trans A 33:1817–1830. doi:10.1007/s11661-002-0190-6

    Article  Google Scholar 

  3. Jin X, Li L, Zhang Y (2003) A heat transfer model for deep penetration laser welding based on an actual keyhole. Int J Heat Mass Transf 46:15–22. doi:10.1016/S0017-9310(02)00255-7

    Article  Google Scholar 

  4. Courtois M, Carin M, Le Masson P et al (2014) A complete model of keyhole and melt pool dynamics to analyze instabilities and collapse during laser welding. J Laser Appl 26:042001. doi:10.2351/1.4886835

    Article  Google Scholar 

  5. Tan W, Bailey NS, Shin YC (2013) Investigation of keyhole plume and molten pool based on a three-dimensional dynamic model with sharp interface formulation. J Phys D Appl Phys 46:55501. doi:10.1088/0022-3727/46/5/055501

    Article  Google Scholar 

  6. Huang W, Kovacevic R (2011) A neural network and multiple regression method for the characterization of the depth of weld penetration in laser welding based on acoustic signatures. J Intell Manuf 22:131–143. doi:10.1007/s10845-009-0267-9

    Article  Google Scholar 

  7. Singh A, Cooper DE, Blundell NJ et al (2014) Modelling of weld-bead geometry and hardness profile in laser welding of plain carbon steel using neural networks and genetic algorithms. Int J Comput Integr Manuf 27:656–674. doi:10.1080/0951192X.2013.834469

    Article  Google Scholar 

  8. Luo M, Shin YC (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. Int J Adv Manuf Technol 81:263–276. doi:10.1007/s00170-015-7079-1

    Article  Google Scholar 

  9. Fabbro R, Slimani S, Doudet I et al (2006) Experimental study of the dynamical coupling between the induced vapour plume and the melt pool for Nd–Yag CW laser welding. J Phys D Appl Phys 39:394–400. doi:10.1088/0022-3727/39/2/023

    Article  Google Scholar 

  10. Mościcki T, Hoffman J, Szymanski Z (2006) Modeling of plasma plume induced during laser welding. In: AIP Conf. Proc. AIP., pp 165–168

    Chapter  Google Scholar 

  11. Volpp J (2012) Investigation on the influence of different laser beam intensity distributions on keyhole geometry during laser welding. Phys Procedia 39:17–26. doi:10.1016/j.phpro.2012.10.009

    Article  Google Scholar 

  12. Akman E, Demir A, Canel T, Sinmazçelik T (2009) Laser welding of Ti6Al4V titanium alloys. J Mater Process Technol 209:3705–3713. doi:10.1016/j.jmatprotec.2008.08.026

    Article  Google Scholar 

  13. Lisiecki A (2012) Welding of titanium alloy by different types of lasers. Arch Mater Sci Eng 58:209–218. doi:10.2478/amm-2014-0276

    Google Scholar 

  14. Gao X-L, Zhang L-J, Liu J, Zhang J-X (2014) Effects of weld cross-section profiles and microstructure on properties of pulsed Nd:YAG laser welding of Ti6Al4V sheet. Int J Adv Manuf Technol 72:895–903. doi:10.1007/s00170-014-5722-x

    Article  Google Scholar 

  15. Lisiecki A (2013) Welding of titanium alloy by disk laser. In: Woliński WL, Jankiewicz Z, Romaniuk RS (eds) Laser Technol. 2012 Appl. Lasers. Świnoujście, p 87030T

  16. Shirguppikar SS, Ganachari VS, Dhaingade PS, Apte AD (2014) Study of laser welding technique for titanium alloy sheet. Int J Adv Engg ResStudies III:20–22

  17. Liu Y, Zhang Y (2013) Weld penetration control in gas tungsten arc welding (GTAW) process. In: IECON Proc. (Industrial Electron. Conf.). IEEE, Vienna, pp 3842–3847

    Google Scholar 

  18. Sibillano T, Rizzi D, Mezzapesa FP et al (2012) Closed loop control of penetration depth during CO2 laser lap welding processes. Sensors 12:11077–11090. doi:10.3390/s120811077

    Article  Google Scholar 

  19. Boyer R, Welsch G, Collings EW (1994) Materials properties handbook—titanium alloys. ASM International, Materials Park

    Google Scholar 

  20. Ngo PD, Shin YC (2016) Modelling of unstructured uncertainties and robust controlling of nonlinear dynamic systems based on type-2 fuzzy basis function networks. Eng Appl Artif Intell 53:74–85, doi:10.1016/j.engappai.2016.03.010

  21. Lee CW, Shin YC (2003) Construction of fuzzy systems using least-squares method and genetic algorithm. Fuzzy Sets Syst 137:297–323. doi:10.1016/S0165-0114(02)00344-5

    Article  MathSciNet  MATH  Google Scholar 

  22. Subrahmanya N, Shin YC (2009) Adaptive divided difference filtering for simultaneous state and parameter estimation. Automatica 45:1686–1693. doi:10.1016/j.automatica.2009.02.029

    Article  MathSciNet  MATH  Google Scholar 

  23. Stephen B, Laurent EG, Eric F, Venkataramanan B (1994) Linear matrix inequalities in system and control theory. doi: 10.1137/1.9781611970777

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Ngo, P.D., Shin, Y.C. Modeling and robust controlling of laser welding process on high strength titanium alloy using fuzzy basis function networks and robust Takagi-Sugeno fuzzy controller. Int J Adv Manuf Technol 89, 1089–1102 (2017). https://doi.org/10.1007/s00170-016-9104-4

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  • DOI: https://doi.org/10.1007/s00170-016-9104-4

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