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Neural Computing and Applications

, Volume 31, Supplement 2, pp 947–956 | Cite as

Optimization of friction stir welding process using NSGA-II and DEMO

  • Nizar Faisal Alkayem
  • Biswajit Parida
  • Sukhomay PalEmail author
Original Article

Abstract

In welding processes, the selection of optimal process parameter settings is very important to achieve best weld qualities. In this work, neuro-multi-objective evolutionary algorithms (EAs) are proposed to optimize the process parameters in friction stir welding process. Artificial neural network (ANN) models are developed for the simulation of the correlation between process parameters and mechanical properties of the weld using back-propagation algorithm. The weld qualities of the weld joint, such as ultimate tensile strength, yield stress, elongation, bending angle and hardness of the nugget zone, are considered. In order to optimize those quality characteristics, two multi-objective EAs that are non-dominated sorting genetic algorithm II and differential evolution for multi-objective are coupled with the developed ANN models. In the end, multi-criteria decision-making method which is technique for order preference by similarity to the ideal solution is applied on the Pareto front to extract the best solutions. Comparisons are conducted between results obtained from the proposed techniques, and confirmation experiments are performed to verify the simulated results.

Keywords

Friction stir welding Artificial neural network NSGA-II DEMO TOPSIS 

Notes

Acknowledgements

The authors gratefully acknowledge the financial support provided by SERB (Science and Engineering Research Board), India (Grant no. SERB/F/2767/2012-13), to carry out this research work.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

References

  1. 1.
    Thomas W, Nicholas E, Needham J, Murch M, Templesmith P, Dawes C (1991) Friction stir welding. UK Patent international patent application no. PCT/GB92102203 and Great Britain Patent application no. 9125978.8., 1991Google Scholar
  2. 2.
    Neto DM, Neto P (2013) Numerical modeling of the friction stir welding process: a literature review. Int J Adv Manuf Technol 65:115–126CrossRefGoogle Scholar
  3. 3.
    Mishra R, Mahoney M (2007) Friction stir welding and processing. ASM International, OhioGoogle Scholar
  4. 4.
    Boldsaikhan E, Corwin E, Logar A, Arbegast W (2001) The use of neural network and discrete Fourier transform for real-time evaluation of friction stir welding. Appl Soft Comput 11:4839–4846CrossRefGoogle Scholar
  5. 5.
    Lakshminarayanan A, Balasubramanian V (2009) Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA7039 aluminium alloy joints. Trans Nonferr Met Soc 19:9–18CrossRefGoogle Scholar
  6. 6.
    Buffa G, Fratini L, Micari F (2012) Mechanical and microstructural properties prediction by artificial neural networks in FSW processes of dual phase titanium alloys. J Manuf Process 14:289–296CrossRefGoogle Scholar
  7. 7.
    Okuyucu H, Kurt A, Arcaklioglu E (2007) Artificial neural network application to the friction stir welding of aluminum plates. Mater Des 28:78–84CrossRefGoogle Scholar
  8. 8.
    Fratini L, Buffa G, Palmeri D (2009) Using a neural network for predicting the average grain size in friction stir welding processes. Comput Struct 87:1166–1174CrossRefGoogle Scholar
  9. 9.
    Ghetiya ND, Patel K (2014) Prediction of tensile strength in friction stir welded aluminium alloy using artificial neural network. Proc Technol 14:274–281CrossRefGoogle Scholar
  10. 10.
    Asadi P, Besharati Givi MK, Rastgoo A, Akbari M, Zakeri V, Rasouli R (2012) Predicting the grain size and hardness of AZ91/SiC nanocomposite by artificial neural networks. Int J Adv Manuf Technol 63:1095–1107CrossRefGoogle Scholar
  11. 11.
    Akbari M, Asadi P, Besharati-Givi MK, Khodabandehlouie G (2014) Artificial neural network and optimization. In: Besharati-Givi MK, Asadi P (eds) Advances in friction-stir welding and processing. Woodhead Publishing, pp 543–599. doi: 10.1533/9780857094551.543
  12. 12.
    Alkayem NF, Parida B, Pal S (2016) Optimization of friction stir welding process parameters using soft computing techniques. Soft Comput. doi: 10.1007/s00500-016-2251-6 Google Scholar
  13. 13.
    Deb K (2011) Multi-objective optimization using evolutionary algorithms. Wiley India, New DelhizbMATHGoogle Scholar
  14. 14.
    Deb K, Pratap A, Agarwal S, Meyarivan T (2012) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRefGoogle Scholar
  15. 15.
    Coello C, Lechuga M (2002) MOPSO: a proposal for multiple-objective particle swarm optimization. In: 2002 IEEE congress on evolutionary computation (CEC)Google Scholar
  16. 16.
    Robič T, Filipič B, (2005) DEMO: differential evolution for multiobjective optimization. In: 2005 the 3rd International conference on evolutionary multi-criterion optimizationGoogle Scholar
  17. 17.
    Shojaeefard M, Behnagh R, Akbari M, Givi M, Farhani F (2013) Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm. Mater Des 44:190–198CrossRefGoogle Scholar
  18. 18.
    Tutum C, Hattel J (2010) A multi-objective optimization application in friction stir welding: considering thermo-mechanical aspects. In: 2010 IEEE congress on evolutionary computation (CEC)Google Scholar
  19. 19.
    Shojaeefard M, Akbari M, Asadi P (2014) Multi objective optimization of friction stir welding parameters using FEM and neural network. Int J Precis Eng Manuf 15(11):2351–2356CrossRefGoogle Scholar
  20. 20.
    Haykin S (2003) Neural networks—a comprehensive foundation, 2nd edn. Pearson Education, New DelhizbMATHGoogle Scholar
  21. 21.
    Hwang C, Yoon K (1981) Multiple attribute decision making: methods and applications. Springer-Verlag, New YorkCrossRefzbMATHGoogle Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Nizar Faisal Alkayem
    • 1
    • 2
  • Biswajit Parida
    • 1
  • Sukhomay Pal
    • 1
    Email author
  1. 1.Department of Mechanical EngineeringIIT GuwahatiGuwahatiIndia
  2. 2.Department of Engineering MechanicsHohai UniversityNanjingChina

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