Multi-objective Optimization of MIG Welding of Aluminum Alloy

  • Ashish ChafekarEmail author
  • Sagar SapkalEmail author
Conference paper


Aluminum AA6061-T6 (Al-Mg-Si) is a precipitation hardened aluminum alloy belonging to 6000 series having the intermediate strength and possesses excellent corrosion resistance. It has been widely used for fabrication of lightweight structure such as marine structure components, wings, and fuselages of aircraft structure, and bicycle frames etc. which requires a high strength-to-weight ratio. However, MIG welding of AA6061 material is very critical so, the selection of process parameter plays a major role in determining the quality of the weld joint. In this paper input parameter such as welding voltage, wire feed rate, and dynamic or arc force correction have been chosen. The experiments were conducted on semi-automatic pulse MIG welding machine according to the L9 orthogonal array with replication. The process parameters viz. welding voltage, wire feed rate and dynamic is significant in intelligent MIG welding machine have been considered as variables. The responses such as tensile strength, hardness and heat affected zone (HAZ) of welded joints of AA6061-T6 aluminum alloy have been investigated and optimized using Taguchi based grey relational analysis. From this multi objective optimization, it has been found that welding current is the most significant parameter followed by wire feed rate and dynamic factor for the intelligent welding machine under consideration.


Aluminum alloy ANOVA Grey relational analysis Hardness HAZ Tensile strength 


  1. 1.
    Bohnart ER (2014) Welding principles and practices. McGraw Hill Education, New DelhiGoogle Scholar
  2. 2.
    Choudhury B, Chandrasekaran M (2017) Investigation on welding characteristics of aerospace materials-a review. Mat Today Proc 4:7519–7526.. Elsevier, UKCrossRefGoogle Scholar
  3. 3.
    Kim JH, Jo DS (2017) Hardness prediction of weldment in friction stir welding of AA6061 based on numerical approach. Procedia Eng 207:586–590. Elsevier, Cambridge UKCrossRefGoogle Scholar
  4. 4.
    Pal S, Pal SK, Samantaray AK (2017) Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals. J Mater Process Technol 2:464–474. ElsevierGoogle Scholar
  5. 5.
    Trivedi PT, Bhabhor AP (2014) Experimental investigation of process parameters on weld bead geometry for aluminum using GTAW. Int J Sci Res 2:803–809Google Scholar
  6. 6.
    Kanwal VR, Jadoun S (2015) Optimization of MIG welding parameters for hardness of aluminum alloys using Taguchi method. Int J Mech Eng (SSRG-IJME) 2:53–56Google Scholar
  7. 7.
    Sivaraman A, Paulraj S (2017) Multi-response optimization of process parameters for MIG welding of AA2219-T87 by Taguchi grey relational analysis. Mat Today Proc 4:8892–8900. ElsevierCrossRefGoogle Scholar
  8. 8.
    Patel S (2014) An experimental investigation on the effect of MIG welding parameters on the weld joint using Taguchi method. Int J Adv Eng Res Dev 2:104–110Google Scholar
  9. 9.
    Kumar D, Jindal S (2014) Optimization of process parameters of gas metal arc welding by Taguchi’s experimental design method. Int J Surf Eng Mat Technol 4:24–27Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringWalchand College of EngineeringSangliIndia

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