Skip to main content
SpringerLink
Log in

Friction Stir Welding of Aluminum Using a Multi-Objective Optimization Approach Based on Both Taguchi Method and Grey Relational Analysis

  • Research paper
  • Published:
Experimental Techniques Aims and scope Submit manuscript

Abstract

This work deals with the use of a multi-objective optimization method using a hybrid statistic algorithm to improve the Friction Stir Welding of aluminum alloy AA2195-T8. The hybrid approach combines the Taguchi Method with the Relational Grey Analysis technic. In order to optimize the Friction Stir Welding process, the axial force, the rotational tool velocity, the welding velocity and the shoulder diameter were considered as input parameters while the heat input, the maximal temperature value and the Heat Affected Zone length were chosen as output parameters. In this method, the minimization of the heat input, the HAZ length and the temperature value in the stir zone is the main goal. In the process of improving the aluminum welding by FSW, the axial force is the most influential parameter with a contribution of 52.4%, followed by the rotational tool velocity with 37.4%, then the welding velocity with 6.3% and finally the tool diameter with a contribution of 3.6%. The obtained results from the application of three-dimensional numerical thermal model have confirmed the effectiveness and the robustness of the used optimization approach.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Abbreviations

cp(T):

Heat capacity (J.kg−1.K−1).

E:

Energy input (J.m−1).

F:

Applied force (N).

hR :

Specific heat transfer coefficient (W.m−2.K−1).

h∞ :

Heat-transfer coefficient (W.m−2.K−1).

hp :

Height of tool pin (m).

H:

Workpiece thickness (m).

Ly :

Distance between a position y (T = 523 K) and the tool axis (m).

K (ky,kx,kz):

Thermal conductivity and its components (W.m−1.K−1).

q(r):

Heat source model (W.m−2).

Q P :

Pin heat generation (W).

Q S :

Shoulder heat generation (W).

Q Pb :

Heat flux on pin bottom (W).

Q Total :

Total heat flux coming from the friction between the tool and the workpiece (W).

R:

Radiusm.

Rs, Rp :

Shoulder, pin radius (m).

S:

Heat source term (in equation (1)) (W.m−3).

T:

Temperature (K).

T :

Time (s).

T 0 :

Ambient temperature (K).

T m :

Melting temperature (K).

T max :

Maximum welding temperature (K).

T w :

Welding temperature (75% of Tm) (K).

u w :

Welding velocity (m.s−1).

X :

Vector.

X, Y, Z :

Space coordinate (m).

SS:

Sum of squares of any factor

DOF:

Degree Of Freedom of any factor

MS:

Mean squares (variance)

F ratio:

variance or fisher ratio of any factor

SS”:

pure sum of squares of any factor

P %:

Percent of contribution

ANOVA:

Analysis of variance

FSW:

Friction stir welding

GRG:

Grey relational grad

HI:

Heat input

MTV:

Maximum temperature value

HAZ:

Heat affected zone

TMAZ :

Thermomechanical affected zone

BM:

Base material

References

  1. Thomas WM, Nicholas ED, Needham JC, Murch MG, Temple-Smith P, and Dawes CJ, (1995) "Friction welding," ed: Google Patents

  2. Mishra RS, De PS, Kumar N (2014) Friction stir processing. In: In friction stir welding and processing. Springer, Dordrecht, pp 259–296

    Chapter  Google Scholar 

  3. Frigaard Ø, Grong Ø, Midling O (2001) A process model for friction stir welding of age hardening aluminum alloys. Metall Mater Trans A 32(5):1189–1200

    Article  Google Scholar 

  4. Taysom BS, Sorensen CD, Hedengren JD (2017) A comparison of model predictive control and PID temperature control in friction stir welding. J Manuf Process 29:232–241

    Article  Google Scholar 

  5. Tang W, Guo X, McClure J, Murr L, Nunes A (1998) Heat input and temperature distribution in friction stir welding. J Mater Process Manuf Sci 7:163–172

    Article  CAS  Google Scholar 

  6. Pal S, Phaniraj M (2015) Determination of heat partition between tool and workpiece during FSW of SS304 using 3D CFD modeling. J Mater Process Technol 222:280–286

    Article  CAS  Google Scholar 

  7. Lim S, Kim S, Lee C-G, Kim SJ, Yim CD (2005) Tensile behavior of friction-stir-welded AZ31-H24 mg alloy. Metall Mater Trans A 36(6):1609–1612

    Article  Google Scholar 

  8. Nandan R, Roy G, Lienert T, DebRoy T (2006) Numerical modelling of 3D plastic flow and heat transfer during friction stir welding of stainless steel. Sci Technol Weld Join 11(5):526–537

    Article  Google Scholar 

  9. Zhou X, Pan W, MacKenzie D (2013) Identifying friction stir welding process parameters through coupled numerical and experimental analysis. Int J Press Vessel Pip 108:2–6

    Article  Google Scholar 

  10. Boukraa M, Lebaal N, Mataoui A, Settar A, Aissani M, Tala-Ighil N (2018) Friction stir welding process improvement through coupling an optimization procedure and three-dimensional transient heat transfer numerical analysis. J Manuf Process 34:566–578

    Article  Google Scholar 

  11. Boukraa M, Chekifi T, Lebaal N et al (2021) Robust optimization of both dissolution time and heat affected zone over the friction stir welding process using SQP technique. Exp Tech. https://doi.org/10.1007/s40799-021-00515-8

  12. Prajapati V, Vora JJ, Das S, Abhishek K (2020) Study of parametric influence and welding performance optimization during regulated metal deposition (RMD™) using grey integrated with fuzzy taguchi approach. J Manuf Process 54:286–300

    Article  Google Scholar 

  13. Mehdi H, Mishra RS (2020) An experimental analysis and optimization of process parameters of AA6061 and AA7075 welded joint by TIG+FSP welding using RSM. Adv Mater Process Technol. https://doi.org/10.1080/2374068X.2020.1829952

  14. Asmare A, Al-Sabur R, Messele E (2020) Experimental investigation of friction stir welding on 6061-T6 aluminum alloy using Taguchi-based GRA. Metals 10(11):1480

    Article  CAS  Google Scholar 

  15. Shaysultanov D, Raimov K, Stepanov N, Zherebtsov S (2021) Friction stir welding of a trip Fe49Mn30Cr10Co10C1 high entropy alloy

  16. Meseguer-Valdenebro JL, Serna J, Portoles A, Estrems M, Miguel V, Martínez-Conesa E (2016) Experimental validation of a numerical method that predicts the size of the heat affected zone. Optimization of the welding parameters by the Taguchi’s method. Trans Indian Inst Metals 69(3):783–791

    Article  Google Scholar 

  17. Ahmadi H, Arab NM, Ghasemi FA (2014) Optimization of process parameters for friction stir lap welding of carbon fibre reinforced thermoplastic composites by Taguchi method. J Mech Sci Technol 28(1):279–284

    Article  Google Scholar 

  18. Vidal C, Infante V (2013) Optimization of FS welding parameters for improving mechanical behavior of AA2024-T351 joints based on Taguchi method. J Mater Eng Perform 22(8):2261–2270

    Article  CAS  Google Scholar 

  19. Kumar S, Kumar S (2015) Multi-response optimization of process parameters for friction stir welding of joining dissimilar Al alloys by gray relation analysis and Taguchi method. J Braz Soc Mech Sci Eng 37(2):665–674

    Article  CAS  Google Scholar 

  20. Nourani M, Milani AS, Yannacopoulos S (2011) Taguchi optimization of process parameters in friction stir welding of 6061 aluminum alloy: a review and case study. Engineering 3(2):144

    Article  CAS  Google Scholar 

  21. Boukraa M, Bassir D, Lebaal N, Chekifi T, Aissani M, Ighil NT, Mataoui A (2021) Thermal analysis of the friction stir welding process based on boundary conditions and operating parameters. In: Proceedings of the Estonian Academy of Sciences, vol 70, no 4

  22. Boukraa M, Aissani M, Lebaal N, Bassir D, Mataoui A, Ighil NT (2021) Effects of boundary conditions and operating parameters on temperature distribution during the friction stir welding process. In: In IOP conference series: materials science and engineering, 1140th edn. IOP Publishing, Bristol, p 012050

    Google Scholar 

  23. Chekifi T (2018) Computational study of droplet breakup in a trapped channel configuration using volume of fluid method. Flow Meas Instrum 59:118–125

    Article  Google Scholar 

  24. Chekifi T, Boukraa M, Aissani M (2021) DNS using CLSVOF method of single micro-bubble breakup and dynamics in flow focusing. J Vis 24:519–530. https://doi.org/10.1007/s12650-020-00715-1

  25. Chekifi T (2019) Droplet breakup regime in a cross-junction device with lateral obstacles. Fluid Dyn Mater Proc 15(5):545–555

    Google Scholar 

  26. Zhang X, Xiao B, Ma Z (2011) A transient thermal model for friction stir weld. Part II: effects of weld conditions. Metall Mater Trans A 42(10):3229–3239

    Article  CAS  Google Scholar 

  27. Mahoney M, Rhodes C (1998) JG F lintoff, RA S purling, WH B ingel. Metall Mater Trans A 29:1955

    Article  Google Scholar 

  28. Larsen AA, Bendsøe M, Hattel J, Schmidt H (2009) Optimization of friction stir welding using space mapping and manifold mapping—an initial study of thermal aspects. Struct Multidiscip Optim 38(3):289–299

    Article  Google Scholar 

  29. Chao YJ, Qi X, Tang W (2003) Heat transfer in friction stir welding—experimental and numerical studies. J Manuf Sci Eng 125(1):138–145

    Article  Google Scholar 

  30. Jayaraman P, Kumar LM (2014) Multi-response optimization of machining parameters of turning AA6063 T6 Aluminium alloy using Grey relational analysis in Taguchi method. Process Eng 97:197–204

    CAS  Google Scholar 

  31. Aissani M, Gachi S, Boubenider F, Benkedda Y (2010) Design and optimization of friction stir welding tool. Mater Manuf Process 25(11):1199–1205

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Center in Industrial Technologies CRTI, P.O. Box 64, Cheraga, 16014, Algiers, Algeria

    M. Boukraa

  2. Unité de Recherche Appliqué en Energies Renouvelables, URAER, Centre de Développement des Energies Renouvelables, CDER, 47133, Ghardaïa, Algeria

    T. Chekifi

  3. Université Bourgogne Franche-Comté, UTBM, Laboratoire interdisciplinaire Carnot de Bourogne, UMR CNRS6303/90010, Belfort, France

    N. Lebaal

Authors
  1. M. Boukraa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. T. Chekifi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. N. Lebaal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. Chekifi.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Boukraa, M., Chekifi, T. & Lebaal, N. Friction Stir Welding of Aluminum Using a Multi-Objective Optimization Approach Based on Both Taguchi Method and Grey Relational Analysis. Exp Tech 47, 603–617 (2023). https://doi.org/10.1007/s40799-022-00573-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40799-022-00573-6

Keywords

Search

Navigation