, Volume 11, Issue 1, pp 459–469 | Cite as

Application of Genetic Algorithm Optimization Technique in TIG Welding of 15CDV6 Aerospace Steel

  • L. SrinivasanEmail author
  • Mohammad Chand Khan
  • T. Deepan Bharathi Kannan
  • P. Sathiya
  • S. Biju
Original Paper


In this work, an attempt is made to identify the optimized parameter combinations in TIG (Tungsten Inert Gas) welding of 15CDV6 steel. 15CDV6 is a high strength low alloy steel which is widely used in aerospace industries. TIG welding was carried out on 15CDV6 steel with current, voltage, welding speed, shielding gas flow rate as input parameters. Tensile strength, hardness, and corrosion current density were measured as performance characteristics. Artificial Neural Network (ANN) was used as modeling technique for predicting the output parameters. Four different learning algorithms, viz. Batch Back Propagation (BBP), Quick Propagation (QP), Incremental Back Propagation (IBP), Legvenberg- Marquardt (LM) back propagation were used to train the model, and it was found that legvenberg- Marquardt (LM) as the best learning algorithm concerning the prediction accuracy. Genetic algorithm (GA) optimization technique was used to identify the optimized parameter combinations, and a confirmation test was carried out with GA predicted optimized parameters. The confirmation test results were in good agreement with GA results.


15CDV6 TIG welding ANN GA 


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  1. 1.
    Bandyopadhyay TR, Rao PK, Prabhu N (2011) ISRN Mat Sci 1:1–7Google Scholar
  2. 2.
    Sapthagiri S, Jayathirtha Rao K, Ashok Reddy K, Sharada Prabhakar C (2015) IJARF 2(5):16–20Google Scholar
  3. 3.
    Neelamegam C, Vardhan Sapineni V, Muthukumaran V, Tamana J (2013) J Intell Learn Syst Appl 5:39Google Scholar
  4. 4.
    Indhumathi SN, Vasudevan T, Sundarajan S, Subba Rao BV (2011) Met Finish 109:15CrossRefGoogle Scholar
  5. 5.
    Deepan Bharathi Kannan T, Ramesh T, Sathiya P (2016), Trans Indian Inst Met.
  6. 6.
    Katherasan D, Elias JV, Sathiya P, Noorul Haq A (2012) J Intell Manuf.
  7. 7.
    Deepan Bharathi Kannan T, Rajesh Kannan G, Suresh Kumar B, Baskar N (2014) Proc Mat Sci 5:2242Google Scholar
  8. 8.
    Park H, Rhee S (1999) J Laser Appl 11(3):143CrossRefGoogle Scholar
  9. 9.
    Sathiya P, Panneerselvam K, Abdul Jaleel MY (2012) Mat Des 36:490CrossRefGoogle Scholar
  10. 10.
    Park YW, Rhee S (2008) Int J Adv Manuf Technol 37:1014CrossRefGoogle Scholar
  11. 11.
    Hayajneh MT, Hassan AM, Mayyas AT (2009) J All Comput 478:559CrossRefGoogle Scholar
  12. 12.
    Kamal Babu K, Panneerselvam K, Sathiya P, Noorul Haq A, Sundarrajan S, Mastanaiah P, Srinivasa Murthy CV (2017) Int J Adv Manuf Technol.
  13. 13.
    Zhao D, Ren D, Zhao K, Pan S, Guoa X (2017) J Manuf Proc 30:63CrossRefGoogle Scholar
  14. 14.
    Rong Y, Zhou Q, Huang Y, Chang Y, Zhang G, Shao X (2016) Int J Adv Manuf Technol 85:1239CrossRefGoogle Scholar
  15. 15.
    Wang Z-H, Gong D-Y, Li X, Li G-T, Zhang D-H (2017) Int J Adv Manuf Technol 93:3325CrossRefGoogle Scholar
  16. 16.
    Zhanga K, Chena Y, Zhenga J, Huanga J, Tanga X (2017) J Manuf Proc 30:553CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • L. Srinivasan
    • 1
    Email author
  • Mohammad Chand Khan
    • 2
  • T. Deepan Bharathi Kannan
    • 3
  • P. Sathiya
    • 1
  • S. Biju
    • 4
  1. 1.Department of Production EngineeringNational Institute of TechnologyTrichyIndia
  2. 2.Department of Mechanical EngineeringK. Ramakrishnan College of EngineeringTrichyIndia
  3. 3.Department of Mechanical EngineeringSRM Institute of Science & TechnologyKattankulathurIndia
  4. 4.Vikram Sarabhai Space CenterISROThiruvananthapuramIndia

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