Skip to main content
SpringerLink
Log in

A new simulation-based metaheuristic approach in optimization of bilayer composite sheet hydroforming

  • Technical Paper
  • Published:
Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

Simulated annealing which has been recently used in engineering applications is one of the most popular metaheuristic algorithms to solve optimization problems. In this work, an adaptive simulated annealing (SA) technique coupled with an adaptive finite element method is proposed and developed to optimize bilayer composite sheet hydroforming process. The aim of this combinatorial procedure is to obtain optimal forming pressure loading path to develop a new definition of SA algorithm parameters, to create an adaptive finite element (FE) code, to produce desired products with minimum thinning, and to avoid wrinkling and bursting failures. All of the effective SA parameters including Markov chain number, cooling function, initial temperature, and final temperature are redefined and developed to reduce excess iterations in FE simulations, hence saving the time and energy during the optimization process. The proposed optimization technique is employed to investigate the effect of layers sequence and thickness ratio on minimum cup thinning and optimal pressure load in hydroforming of aluminum/steel composite sheet. The experimental results demonstrated that the proposed adaptive method could be a reliable technique for optimization of processes with a large search space. It is also shown that it provides precise results in a shorter time by redefining the algorithm parameters.

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

Similar content being viewed by others

Notes

  1. North American Deep Drawing Research Group.

References

  1. Afshin E, Kadkhodayan M (2015) An experimental investigation into the warm deep-drawing process on laminated sheets under various grain sizes. Mater Des 87:25–35

    Article  Google Scholar 

  2. Atrian A, Fereshteh-Saniee F (2013) Deep drawing process of steel/brass laminated sheets. Compos B 47:75–81

    Article  Google Scholar 

  3. Parsa MH, Yamaguchi K, Takakura N (2001) Redrawing analysis of aluminum–stainless-steel laminated sheet using FEM simulations and experiments. Int J Mech Sci 43:2331–2347

    Article  MATH  Google Scholar 

  4. Lang L, Danckert J, Nielsen KB (2005) Multi-layer sheet hydroforming: experimental and numerical investigation into the very thin layer in the middle. J Mater Process Technol 170:524–535

    Article  Google Scholar 

  5. Bagherzadeh S, Mirnia MJ, Mollaei-Dariani B (2015) Numerical and experimental investigations of hydro-mechanical deep drawing process of laminated aluminum/steel sheets. J Manuf Process 18:131–140

    Article  Google Scholar 

  6. Hashemi A, Hoseinpour-Gollo M, Seyedkashi SMH (2015) Study of Al/St laminated sheet and constituent layers in radial pressure assisted hydrodynamic deep drawing. Mater Manuf Process. doi:10.1080/10426914.2015.1127947

    Google Scholar 

  7. Kirkpatrick S, Gelatt CD, Vecchi MP (1983) Optimization by simulated annealing. Science 220:671–680

    Article  MathSciNet  MATH  Google Scholar 

  8. Seyedkashi SMH, Moslemi-Naeini H, Liaghat GH, Mosavi-Mashhadi M, Mirzaali M, Shojaee K, Moon YH (2012) The effect of tube dimensions on optimized pressure and force loading paths in tube hydroforming process. J Mech Sci Technol 26:1817–1822

    Article  Google Scholar 

  9. Yang XSh (2014) Nature-inspired optimization algorithms. Elsevier Science, Burlington

    MATH  Google Scholar 

  10. Mirzaali M, Seyedkashi SMH, Liaghat GH, Moslemi-Naeini H, Shojaee K, Moon YH (2012) Application of simulated annealing method to pressure and force loading optimization in tube hydroforming process. Int J Mech Sci 55:78–84

    Article  Google Scholar 

  11. Hashemi A, Mashhadi M, Bakhshi-Jooybari M, Gorji A (2012) Study of the effect of material properties and sheet thickness on formability of conical parts in hydro-mechanical deep drawing assisted by radial pressure. Adv Mater Res 445:149–154

    Article  Google Scholar 

  12. Seyedkashi SMH, Moslemi-Naeini H, Liaghat GH, Mosavi-Mashadi M, Shojaee-Ghandashtani K, Mirzaali M, Moon YH (2012) Experimental and numerical investigation of simulated annealing technique in optimization of warm tube hydroforming. Proc Inst Mech Eng B J Eng Manuf 226:1869–1879

    Article  Google Scholar 

  13. Seyedkashi SMH, Moslemi-Naeini H, Moon YH (2014) Feasibility study on optimized process conditions in warm tube hydroforming. J Mech Sci Technol 28:2845–2852

    Article  Google Scholar 

  14. Chibante R, Araújo A, Carvalho A (2010) Parameter identification of power semi-conductor device models using metaheuristics. In: Chibante R (ed) Simulated annealing theory with application, 1st edn. Sciyo, Croatia, pp 1–16

    Chapter  Google Scholar 

  15. Simopoulos DN, Kavatza SD, Vournas CD (2006) Unit commitment by an enhanced simulated annealing algorithm. IEEE Trans Power Syst 21:68–76

    Article  Google Scholar 

  16. Smith KI, Everson RM, Fieldsend JE, Murphy C, Misra R (2008) Dominance-based multiobjective simulated annealing. IEEE Trans Evol Comput 12:323–342

    Article  Google Scholar 

  17. Shojaee K, Shakouri H, Behnam M (2010) Importance of the initial conditions and the time schedule in the simulated annealing: a mushy state SA for TSP. In: Chibante R (ed) Simulated annealing theory with applications, 1st edn. Sciyo, Croatia, pp 217–233

    Google Scholar 

  18. Wang Y, Li F, Wan Q, Chen H (2011) Reactive power planning based on fuzzy clustering, gray code, and simulated annealing. IEEE Trans Power Syst 26:2246–2255

    Article  Google Scholar 

  19. Jalil A, Hoseinpour-Gollo M, Sheikhi MM, Seyedkashi SMH (2016) Hydrodynamic deep drawing of double layered conical cups. Trans Nonferrous Met Soc China 26:237–247

    Article  Google Scholar 

  20. Hashemi A, Hoseinpour-Gollo M, Seyedkashi SMH (2015) Process window diagram of conical cups in hydrodynamic deep drawing assisted by radial pressure. Trans Nonferrous Met Soc China 25:3064–3071

    Article  Google Scholar 

  21. Mirzaali M, Liaghata GH, Moslemi-Naeini H, Seyedkashi SMH, Shojaee K (2011) Optimization of tube hydroforming process using simulated annealing algorithm. Procedia Eng 10:3012–3019

    Article  Google Scholar 

  22. Liu X, Xu Y, Yuan Sh (2008) Effects of loading paths on hydrodynamic deep drawing with independent radial hydraulic pressure of aluminum alloy based on numerical simulation. J Mater Sci Technol 24:395–399

    Google Scholar 

  23. Bayraktar E, Isac N, Arnold G (2005) An experimental study on the forming parameters of deep-drawable steel sheets in automotive industry. J Mater Process Technol 162–163:471–476

    Article  Google Scholar 

  24. Djavanroodi F, Abbasnejad DS, Nezami EH (2011) Deep drawing of aluminum alloys using a novel hydroforming tooling. Mater Manuf Process 26:796–801

    Article  Google Scholar 

  25. Janbakhsh M, Loghmanian SMR, Djavanroodi F (2014) Application of different Hill’s yield criteria to predict limit strains for aerospace titanium and aluminum sheet alloys. Int J Adv Des Manuf Technol 7:35–44

    Google Scholar 

  26. Choi H, Koc M, Ni J (2007) Determination of optimal loading profiles in warm hydroforming of lightweight materials. J Mater Process Technol 190:230–242

    Article  Google Scholar 

  27. Teng B, Li K, Yuan Sh (2013) Optimization of loading path in hydroforming T shape using fuzzy control algorithm. Int J Adv Manuf Technol 69:1079–1086

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by Shahid Rajaee Teacher Training University under contract number 27772. The authors would like to acknowledge this support.

Author information

Authors and Affiliations

  1. Faculty of Mechanical Engineering, Shahid Rajaee Teacher Training University, Tehran, 16785-136, Iran

    Abbas Hashemi, Mohammad Hoseinpour-Gollo & Ali Pourkamali-Anaraki

  2. Department of Mechanical Engineering, University of Birjand, Birjand, 97175-376, Iran

    S. M. Hossein Seyedkashi

Authors
  1. Abbas Hashemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohammad Hoseinpour-Gollo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. M. Hossein Seyedkashi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ali Pourkamali-Anaraki
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mohammad Hoseinpour-Gollo.

Additional information

Technical Editor: Márcio Bacci da Silva.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hashemi, A., Hoseinpour-Gollo, M., Seyedkashi, S.M.H. et al. A new simulation-based metaheuristic approach in optimization of bilayer composite sheet hydroforming. J Braz. Soc. Mech. Sci. Eng. 39, 4011–4020 (2017). https://doi.org/10.1007/s40430-017-0720-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40430-017-0720-1

Keywords

Search

Navigation