Skip to main content

Optimization of Process Parameters in Resistance Spot Welding Using Artificial Immune Algorithm

  • Conference paper
  • First Online:
Innovative Product Design and Intelligent Manufacturing Systems

Abstract

Welding is one of the fundamental manufacturing processes and is used for manufacturing components or assemblies with great strength in minimal time. Resistance spot welding (RSW) is utilized often as an efficacious method of joining for different works, most commonly in automobile and other industrial processes. Recent researches in welding are trending toward the economical process with optimum productivity. It is laborious to formulate a mathematical model for the analysis of RSW parameters, because of obscureness during the process with many parameters especially with the property of less operating time. A novel optimization method based on artificial immune algorithm (AIA) is presented in this article to find the optimum set of welding parameters for an economical process which offers the highest load carrying capacity at low power consumption.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Jou M (2003) Real-time monitoring weld quality of resistance spot welding for the fabrication of sheet metal assemblies. J Mater Process Technol 132(1–3):102–113

    Article  Google Scholar 

  2. Tsai CL, Papritan JC, Dickinson DW, Jammal O (1992) Modeling of resistance spot weld nugget growth. Weld J 71(2):47

    Google Scholar 

  3. Ates H (2007) Prediction of gas metal arc welding parameters based on artificial neural networks. Mater Des 28(7):2015–2023

    Article  Google Scholar 

  4. Cevik A, Kutuk MA, Erklig A, Guzelbey IH (2008) Neural network modeling of arc spot welding. J Mater Process Technol 202(1–3):137–144

    Article  Google Scholar 

  5. Kim IS, Son JS, Yarlagadda PKDV (2003) A study on the quality improvement of robotic GMA welding process. Robot Comput Integr Manuf 19(6):567–572

    Article  Google Scholar 

  6. Darwish SM, Al-Dekhial SD (1999) Statistical models for spot welding of commercial aluminum sheets. Int J Mach Tools Manuf 39(10):1589–1610

    Article  Google Scholar 

  7. Tseng HY (2006) Welding parameters optimization for an economic design using neural approximation and genetic algorithm. Int J Adv Manuf Technol 27(9–10):897–901

    Article  Google Scholar 

  8. Bouyousfi B, Sahraoui T, Guessasma S, Chaouch KT (2007) Effect of process parameters on the physical characteristics of spot weld joints. Mater Des 28(2):414–419

    Article  Google Scholar 

  9. Kahraman N (2007) The influence of welding parameters on the joint strength of resistance spot-welded titanium sheets. Mater Des 28(2):420–427

    Article  Google Scholar 

  10. Hamedi M, Pashazadeh H (2008) Numerical study of nugget formation in resistance spot welding. Int J Mech 2(1):11–15

    Google Scholar 

  11. Pouranvari M (2011) Prediction of failure mode in AISI 304 resistance spot welds. Assoc Metall Eng Serbia Predict

    Google Scholar 

  12. Panda BN, Raju Bahubalendruni MVA, Biswal BB (2014) Optimization of resistance spot welding parameters using differential evolution algorithm and GRNN. In: 2014 IEEE 8th international conference on intelligent systems and control: green challenges and smart solutions, ISCO 2014—proceedings, pp 50–55

    Google Scholar 

  13. Lin WM, Gow HJ, Tsai MT (2011) An efficient hybrid Taguchi-immune algorithm for the unit commitment problem. Expert Syst Appl 38(11):13662–13669

    Google Scholar 

  14. Bakhouya M, Gaber J (2007) An immune inspired-based optimization algorithm: application to the traveling salesman problem. Adv Model Opt 9(1):105–116

    MathSciNet  MATH  Google Scholar 

  15. Aickelin U, Dasgupta D (2013) Search methodologies : introductory tutorials in optimization and decision support techniques. Artificial immune systems. In: In: Burke EK, Kendall G (eds) Search methodologies—introductory tutorials in optimization and decision support technology, 2005, pp 1–29

    Google Scholar 

  16. Cisar P, Cisar SM, Markoski B (2014) Implementation of immunological algorithms in solving optimization problems. Acta Polytech Hungarica 11(4):225–239

    Google Scholar 

  17. Syahputra R, Soesanti I (2017) An artificial immune system algorithm approach for reconfiguring distribution network. AIP Conf Proc 1867

    Google Scholar 

  18. Wang M, Feng S, He C, Li Z, Xue Y (2017) An artificial immune system algorithm with social learning and its application in industrial PID controller design. Math Probl Eng 2017:1–13

    Google Scholar 

  19. Raju Bahubalendruni MVA, Deepak BBVL, Biswal BB (2016) An advanced immune-based strategy to obtain an optimal feasible assembly sequence. Assem Autom 36(2):127–137

    Google Scholar 

  20. Zhang Z, Liao M, Wang L (2012) Immune optimization approach for dynamic constrained multi-objective multimodal optimization problems. Am J Oper Res 02(02):193–202

    Google Scholar 

  21. Zandieh M, Fatemi Ghomi SMT, Moattar Husseini SM (2006) An immune algorithm approach to hybrid flow shops scheduling with sequence-dependent setup times. Appl Math Comput 180(1):111–127

    Google Scholar 

  22. Mohammad R, Akbarzadeh T, Davarzani Z, Khairdoost N (2012) Multiobjective artificial immune algorithm for flexible job shop scheduling problem. Int J Hybrid Inf Technol 5(3):75–88

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Uppada, S., Suraparaju, S.K., Bahubalendruni, M.V.A.R., Natarajan, S.K. (2020). Optimization of Process Parameters in Resistance Spot Welding Using Artificial Immune Algorithm. In: Deepak, B., Parhi, D., Jena, P. (eds) Innovative Product Design and Intelligent Manufacturing Systems. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-15-2696-1_47

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-2696-1_47

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2695-4

  • Online ISBN: 978-981-15-2696-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics