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Optimization of Weld-Bead Parameters of Plasma Arc Welding Using GA and IWO

  • Kadivendi SrinivasEmail author
  • Pandu R. Vundavilli
  • M. Manzoor Hussain
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

Plasma arc welding (PAW) of Inconel 617 plates is an important and critical process for many engineering applications such as combustion cans, high-temperature nuclear reactors, and transition liners in aircraft due to its high depth-to-width ratio. Therefore, finding the combination of optimal input process parameters of the said welding process is an essential task to be carried out before employing it in various applications. In the present study, bead-on-plate (BoP) trails of PAW are performed on Inconel 617 plates after conducting the experiments designed based on the central composite design of experiments (CCD). During experimentation, welding speed, welding current, and gas flow rate are considered as input process parameters, and bead width and bead height of BoP trails are treated as responses of the PAW process. The nonlinear regression equations developed for both the bead width and bead height are optimized with the help of two population-based optimization algorithms, namely genetic algorithm (GA) and invasive weed optimization (IWO) algorithms.

Keywords

Plasma arc welding Bead-on-plate trails Optimization Genetic algorithm Invasive weed optimization 

References

  1. 1.
    Babu KK, Panneerselvam K, Sathiya P, Haq AN, Sundarrajan S, Mastanaiah P, Murthy CS (2018) Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm. Int J Adv Manuf Technol 94(9–12):3117–3129CrossRefGoogle Scholar
  2. 2.
    Nagaraju S, Vasantharaja P, Chandrasekhar N, Vasudevan M, Jayakumar T (2016) Optimization of welding process parameters for 9Cr-1Mo steel using RSM and GA. Mater Manuf Processes 31(3):319–327CrossRefGoogle Scholar
  3. 3.
    Pal S, Pal SK, Samantaray AK (2010) Determination of optimal pulse metal inert gas welding parameters with a neuro-GA technique. Mater Manuf Processes 25(7):606–615CrossRefGoogle Scholar
  4. 4.
    Satpathy MP, Moharana BR, Dewangan S, Sahoo SK (2015) Modeling and optimization of ultrasonic metal welding on dissimilar sheets using fuzzy based genetic algorithm approach. Eng Sci Technol Int J 18(4):634–647CrossRefGoogle Scholar
  5. 5.
    Kanigalpula PKC, Pratihar DK, Jha MN, Derose J, Bapat AV, Pal AR (2016) Experimental investigations, input-output modeling and optimization for electron beam welding of Cu-Cr-Zr alloy plates. Int J Adv Manuf Technol 85(1–4):711–726CrossRefGoogle Scholar
  6. 6.
    Vasudevan M, Bhaduri AK, Raj B, Rao KP (2007) Genetic-algorithm-based computational models for optimizing the process parameters of A-TIG welding to achieve target bead geometry in type 304 L (N) and 316 L (N) stainless steels. Mater Manuf Processes 22(5):641–649CrossRefGoogle Scholar
  7. 7.
    Dey V, Pratihar DK, Datta GL, Jha MN, Saha TK, Bapat AV (2009) Optimization of bead geometry in electron beam welding using a genetic algorithm. J Mater Process Technol 209(3):1151–1157CrossRefGoogle Scholar
  8. 8.
    Kim D, Rhee S, Park H (2002) Modelling and optimization of a GMA welding process by genetic algorithm and response surface methodology. Int J Prod Res 40(7):1699–1711CrossRefGoogle Scholar
  9. 9.
    Meran C (2006) Prediction of the optimized welding parameters for the joined brass plates using genetic algorithm. Mater Des 27(5):356–363CrossRefGoogle Scholar
  10. 10.
    Correia DS, Gonçalves CV, da Cunha Jr SS, Ferraresi VA (2005) Comparison between genetic algorithms and response surface methodology in GMAW welding optimization. J Mater Process Technol 160(1):70–76CrossRefGoogle Scholar
  11. 11.
    Mehrabian AR, Lucas C (2006) A novel numerical optimization algorithm inspired from weed colonization. Ecol Inform 1(4):355–366CrossRefGoogle Scholar
  12. 12.
    Pourjafari E, Mojallali H (2012) Solving nonlinear equations systems with a new approach based on invasive weed optimization algorithm and clustering. Swarm Evol Comput 4:33–43CrossRefGoogle Scholar
  13. 13.
    Dhinakaran V, Shanmugam NS, Sankaranarayanasamy K (2017) Experimental investigation and numerical simulation of weld bead geometry and temperature distribution during plasma arc welding of thin Ti-6Al-4V sheets. J Strain Anal Eng Des 52(1):30–44CrossRefGoogle Scholar
  14. 14.
    Dhinakaran V, Shanmugam NS, Sankaranarayanasamy K (2017) Some studies on temperature field during plasma arc welding of thin titanium alloy sheets using parabolic Gaussian heat source model. Proc Inst Mech Eng Part C J Mech Eng Sci 231(4):695–711CrossRefGoogle Scholar
  15. 15.
    Pratihar DK (2014) Soft computing: fundamentals and applications. Alpha Science International Ltd., Oxford, U.KGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Kadivendi Srinivas
    • 1
    Email author
  • Pandu R. Vundavilli
    • 2
  • M. Manzoor Hussain
    • 3
  1. 1.Department of Mechanical EngineeringDVR & Dr. HS MIC College of TechnologyKanchikacherlaIndia
  2. 2.School of Mechanical SciencesIndian Institute of Technology BhubaneswarBhubaneswarIndia
  3. 3.Department of Mechanical EngineeringJawaharlal Nehru Technological UniversityHyderabadIndia

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