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


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.


Friction stir welding Artificial neural network NSGA-II DEMO TOPSIS 



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.


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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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