Advertisement

Optimization of quality characteristics parameters in a pulsed metal inert gas welding process using grey-based Taguchi method

  • Sukhomay Pal
  • Santosh K. Malviya
  • Surjya K. Pal
  • Arun K. Samantaray
ORIGINAL ARTICLE

Abstract

Optimization of a manufacturing process has to take into accounts all of the factors that influence the product quality and productivity. Optimization of welding process parameters is considerably complex because welding is a multi-variable process, which is influenced by a lot of process uncertainties. In this paper, a grey-based Taguchi method has been adopted to optimize the pulsed metal inert gas welding process parameters. Many quality characteristic parameters are combined into one integrated quality parameter by using grey relational grade or rank. The welding process parameters considered in this analysis are pulse voltage, background voltage, pulse frequency, pulse duty factor, wire feed rate, and table feed rate. The quality parameters considered are the tensile strength, bead geometry, transverse shrinkage, angular distortion, and deposition efficiency. Analysis of variance has been performed to find out the impact of individual process parameter on the quality parameters. If the tensile strength as the most important quality parameter is assigned a higher weight, then the pulse voltage was found to be the most influential process parameter. Experiments with the optimized parameter settings, which have been obtained from the analysis, are given to validate the results.

Keywords

PMIGW Grey-based Taguchi method Orthogonal array ANOVA Weld quality 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brosilow R (1984) Welding better with pulsed power. Weld Des Fabr 5(10):57–69Google Scholar
  2. 2.
    Harvey RC (1995) Gas metal arc welding fume generation using pulsed current. Weld J 74(11):59s–68sMathSciNetGoogle Scholar
  3. 3.
    Palani PK, Muruganb N (2006) Review—selection of parameters of pulsed current gas metal arc welding. J Mater Process Technol 172:1–10. doi: 10.1016/j.jmatprotec.2005.07.013 CrossRefGoogle Scholar
  4. 4.
    Taguchi G (1986) Introduction to quality engineering: designing quality into products and processes. Kraus International, White PlainsGoogle Scholar
  5. 5.
    Pal S, Pal SK, Samantaray AK (2007) Determination of optimal pulse metal inert gas welding parameters using Neuro-GA technique. In: 4th Int Conf on Theoretical, Applied Computational and Experimental Mechanics, IIT Kharagpur, India, December 27–29, ICTACEM-2007/131Google Scholar
  6. 6.
    Roy RK (2001) Design of experiments using the Taguchi approach. Wiley, New YorkGoogle Scholar
  7. 7.
    Anawa EM, Olabi AG (2008) Using Taguchi method to optimize welding pool of dissimilar laser-welded components. Opt Laser Technol 40:379–388. doi: 10.1016/j.optlastec.2007.07.001 CrossRefGoogle Scholar
  8. 8.
    Datta S, Bandyopadhyay A, Pal PK (2007) Grey-based taguchi method for optimization of bead geometry in submerged arc bead-on-plate welding. Int J Adv Manuf Technol 39:1136–1143. doi: 10.1007/s00170-007-1283-6 CrossRefGoogle Scholar
  9. 9.
    Juang SC, Tarng YS (2002) Process parameter selection for optimizing the weld pool geometry in the tungsten inert gas welding of stainless steel. J Mater Process Technol 122:33–37. doi: 10.1016/S0924-0136(02)00021-3 CrossRefGoogle Scholar
  10. 10.
    Tarng YS, Juang SC, Chang CH (2002) The use of grey-based Taguchi methods to determine submerged arc welding process parameters in hardfacing. J Mater Process Technol 128:1–6. doi: 10.1016/S0924-0136(01)01261-4 CrossRefGoogle Scholar
  11. 11.
    Pan LK, Wang CC, Wei SL, Sher HF (2007) Optimizing multiple quality characteristics via Taguchi method-based Grey analysis. J Mater Process Technol 182:107–116. doi: 10.1016/j.jmatprotec.2006.07.015 CrossRefGoogle Scholar
  12. 12.
    Tarng YS, Yang WH, Juang SC (2000) The use of fuzzy logic in the Taguchi method for the optimisation of the submerged arc welding process. Int J Adv Manuf Technol 16:688–694. doi: 10.1007/s001700070040 CrossRefGoogle Scholar
  13. 13.
    Deng J (1982) Control problems of grey systems. Syst Contr Lett 5:288–294Google Scholar
  14. 14.
    Deng J (1989) Introduction to grey theory. J Grey systems 1(1):1–24MATHGoogle Scholar
  15. 15.
    Quinitino L, Allum CJ (1984) Pulsed GMAW—interactions between process parameters—part I. Weld Met Fabrication 52(3):85–89Google Scholar
  16. 16.
    Takeuchi Y, Shinoda T (1991) Spatter and blowhole formation phenomena in pulsed gas shielded metal arc welding. Mater Sci Technol 7:869–875Google Scholar
  17. 17.
    Rao PS (2004) Development of arc rotation mechanism and experimental studies on pulsed GMA welding with and without arc rotation. PhD thesis, IIT Kharagpur, IndiaGoogle Scholar
  18. 18.
    Pal S, Pal SK, Samantaray AK (2008) Sensor based weld bead geometry prediction in pulsed metal inert gas welding process through artificial neural networks. Int J Knowledge-Based Intell Eng Syst 12(2):101–114Google Scholar
  19. 19.
    Pal S, Pal SK, Samantaray AK (2008) Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals. J Mater Process Technol 202(1–3):464–474. doi: 10.1016/j.jmatprotec.2007.09.039 CrossRefGoogle Scholar
  20. 20.
    Raghavendra N, Koranne R, Pal S, Pal SK, Samantaray AK (2008) Joint strength prediction in a pulsed MIG welding process using hybrid optimized model. Int J Adv Manuf Technol. doi: 10.1007/s00170-008-1517-2
  21. 21.
    Pal S, Pal SK, Samantaray AK (2007) Radial basis function neural network model based prediction of weld-plate distortion due to pulsed metal inert gas welding. Sci Tech Weld Join 12(8):725–731. doi: 10.1179/174329307X249351 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Sukhomay Pal
    • 1
  • Santosh K. Malviya
    • 1
  • Surjya K. Pal
    • 1
  • Arun K. Samantaray
    • 1
  1. 1.Department of Mechanical EngineeringIndian Institute of Technology KharagpurKharagpurIndia

Personalised recommendations