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Optimization of activated TIG welding parameters for improving weld joint strength of AISI 4135 PM steel by genetic algorithm and simulated annealing

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Abstract

Weld quality is a very important working aspect of the manufacturing and construction industries. In this research work, an attempt has been made to optimize the parameters of activated tungsten inert gas (A-TIG) welding of sintered hot-forged AISI 4135 steel produced through the powder metallurgy route. Experiments were performed based on Taguchi L9 orthogonal array. Response surface methodology was used to create regression equations, and process parameters were optimized using genetic algorithm (GA) and simulated annealing (SA). Process parameter optimization is multi-input to single output (tensile strength), in which the quality of output depends upon input parameters like current, voltage, welding speed, and gas flow rate. The present study was conducted to maximize the output of the A-TIG welding of sintered hot-forged AISI 4135 steel and fix the input parameters. The results indicate that the voltage and current have a maximum influence on tensile strength in A-TIG-welded joint. Confirmation experiments have also been conducted to validate the optimized parameters.

Keywords

Powder metallurgy A-TIG Tensile strength Taguchi Response surface methodology Genetic algorithm Simulated annealing 

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References

  1. 1.
    Lindberg C (1999) Adv Powder Metall Part Mater 2(7):229–243Google Scholar
  2. 2.
    Karlsson H, Nyborg L, Berg S (2005) Surface chemical analysis of prealloyed water atomised steel powder. Powder Metall 48(1):51–58CrossRefGoogle Scholar
  3. 3.
    Sakthivel T, Vasudevan M, Laha K, Parameswaran P, Chandravathi KS, Mathew MD, Bhaduri AK (2011) Comparison of creep rupture behaviour of type 316L(N) austenitic stainless steel joints welded by TIG and activated TIG welding processes. Mater Sci Eng A 528:6971–6980CrossRefGoogle Scholar
  4. 4.
    Kulikov FR (1968) The use of fluxes for titanium alloy welding. Avtomaticheskaya Svarka 4:9–12 (in Russian)Google Scholar
  5. 5.
    Salak A (1997) Ferrous powder metallurgy. Cambridge International Science Publishing, CambridgeGoogle Scholar
  6. 6.
    Hadrboletz A, Weiss B (1997) Fatigue behavior of iron based sintered materials. A review. Int Mater Rev 42:1–44CrossRefGoogle Scholar
  7. 7.
    Jayabharat K, Ashafaq M, Venugopal P, Achar DRG (2007) Investigation on the continuous drive friction welding of sintered powder metallurgical (P/M) steel and wrought copper parts. Mater Sci Eng 454–455:114–123CrossRefGoogle Scholar
  8. 8.
    Chawla N, Deng X (2005) Microstructure and mechanical behavior of porous sintered steel. Mater Sci Eng 390:98–112CrossRefGoogle Scholar
  9. 9.
    Kurt A, Ates H, Durgutlu A, Karacif K (2004) Exploring the weldability of powder metal parts. Welding J 83(12):34–37Google Scholar
  10. 10.
    Savitsky MM, Leskov GI (1980) Mechanism of effect of electronegative elements on penetrating capacity of arc with tungsten cathode. Avtomatich Svarka 9:17–22Google Scholar
  11. 11.
    Gurevich SM, Zamkov VN (1966) Welding titanium with a non-consumable electrodes with fluxes. Avtomatich Svarka 12:13–16Google Scholar
  12. 12.
    Lucas W, Howse DS (1996) Activating flux—increasing the performance and productivity of the TIG and plasma processes. Weld Met Fabr 64(1):11–17Google Scholar
  13. 13.
    Fujii H, Sato T, Lua S, KiyoshiNogi (2008) Development of an advanced A-TIG (AA-TIG) welding method by control of Marangoni convection. Mater Sci Eng A 495:296–303CrossRefGoogle Scholar
  14. 14.
    Gurevich SV, Zamkov VN, Kushnirenko NA (1996) Improving the penetration of titanium alloys when they are welded by argon tungsten arc process. Automat Weld l.9:1–4Google Scholar
  15. 15.
    Vasudevan M, Bhaduri AK, Baldev Raj (2008) Development and evaluation of activated flux for TIG welding of type 304LN and type 316LN stainless steel. IIW-Int. Conference, Chennai, India, 211–218Google Scholar
  16. 16.
    Yushchenko KA (1993) A-TIG welding of carbon-manganese and stainless steels. Int Con on Welding Technology, Paton Institute, CambridgeGoogle Scholar
  17. 17.
    Palanichamy P, Vasudevan M, Jayakumar T (2009) Measurement of residual stresses in austenitic stainless steel weld joints using ultrasonic technique. Sci Tech Weld Join 14(2):166–171CrossRefGoogle Scholar
  18. 18.
    Ross P (1988) Taguchi technique for quality engineering. McGraw-Hill, New YorkGoogle Scholar
  19. 19.
    Myers RH, Montgomery DH (1995) Response surface methodology. Wiley, New York, 705pMATHGoogle Scholar
  20. 20.
    Kim D, Rhee S (2001) Optimization of arc welding process parameters using a genetic algorithm. Weld J 80(7):184–189Google Scholar
  21. 21.
    Montgomery DC (1984) Design and analysis of experiments, 2nd edn. Wiley, New YorkGoogle Scholar
  22. 22.
    Holland J (1975) Adaptation in natural and artificial systems. The University of Michigan, Ann ArborGoogle Scholar
  23. 23.
    Montgomery DC (2009) Design and analysis of experiments, 7th edn. Wiley, New YorkGoogle Scholar
  24. 24.
    Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Metropolis N, Rosenbluth A, Rosenbluth M, Teller A, Teller E (1953) Equation of state calculations by fast computing machines. J Chem Phys 21:1087–1092CrossRefGoogle Scholar
  26. 26.
    Corana A, Marchesi M, Martini C, Ridella S (1992) Minimizing multimodal function of continuous variables with the simulated annealing algorithm. ACM TOMS 13:262–280MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Miki M, Hiroyasu T, Keiko O (2002) Simulated annealing with advanced adaptive neighborhood. Proc. 2nd Int workshop on intelligent systems design and applications, 113–118, 2002.Google Scholar
  28. 28.
    Miki M, Hiwa S, Hiroyasu T (2006) Simulated annealing using adaptive search vector. IEEE conference on cybernetics and intelligent systems, 1–6Google Scholar
  29. 29.
    Fisher RA (1925) Statistical methods for research worker. Oliver & Boyd, LondonGoogle Scholar

Copyright information

© Springer-Verlag London 2015

Authors and Affiliations

  1. 1.Department of Metallurgical and Materials EngineeringNational Institute of TechnologyTiruchirappalliIndia

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