Optimization of pressure paths in hydrodynamic deep drawing assisted by radial pressure with inward flowing liquid using a hybrid method

  • Milad Sadegh-yazdi
  • Mohammad Bakhshi-Jooybari
  • Mohsen Shakeri
  • Hamid Gorji
  • Maziar Khademi


Hydroforming is a convenient method for applying fluid to produce parts with high strength-to-weight ratio. Hydrodynamic deep drawing assisted by radial pressure with inward flowing liquid process is considered as a type of hydroforming. In this method, radial and chamber pressures are two most important parameters. The values of these parameters at any moment play important roles on the quality of final part. In this study, based on a hybrid method, the chamber and radial pressure paths in hydrodynamic deep drawing assisted by radial pressure with inward flowing liquid process are optimized. In this method, an adaptive simulation that is integrated with the fuzzy control system with the artificial bee colony (ABC) algorithm is used to determine the optimized radial and chamber pressure paths. The achievement of a conical part with minimum thinning and without wrinkling has been defined as the optimization goal. The validity of radial and chamber pressure paths obtained from optimization algorithm is verified through an experiment. Results showed that utilization of the optimized loading path yields a part with lower maximum thinning and without wrinkling.


Hydroforming Optimization Fuzzy control system ABC algorithm Adaptive simulation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Sun Z, Lang L, Li K, Wang Y, Zhang Q (2017) Study on the mechanism and the suppression method of wrinkling in side wall using hydroforming of the fairing. Int J Adv Manuf Technol 90(9–12):2527–2535CrossRefGoogle Scholar
  2. 2.
    Lange K (1985) Handbook of metal forming. McGraw-Hill Book Company, New York, 1985, p 1216Google Scholar
  3. 3.
    Kawka M, Olejnik L, Rosochowski A, Sunaga H, Makinouchi A (2001) Simulation of wrinkling in sheet metal forming. J Mater Process Technol 109(3):283–289CrossRefGoogle Scholar
  4. 4.
    Gorji A, Alavi-Hashemi H, Bakhshi-Jooybari M, Nourouzi S, Hosseinipour SJ (2011) Investigation of hydrodynamic deep drawing for conical–cylindrical cups. Int J Adv Manuf Technol 56(9):915–927CrossRefGoogle Scholar
  5. 5.
    Wang H, Gao L, Chen M (2011) Hydrodynamic deep drawing process assisted by radial pressure with inward flowing liquid. Int J Mech Sci 53(9):793–799CrossRefGoogle Scholar
  6. 6.
    Kesvarakul R, Intarakumthornchai T, Jirathearanat S Semi-forward adaptive simulation approach for tube hydroforming loading path determination using a strain trajectory based fuzzy logic control. In: Applied Mechanics and Materials, 2014. Trans Tech Publ, pp 498–504Google Scholar
  7. 7.
    Assempour A, Emami MR (2009) Pressure estimation in the hydroforming process of sheet metal pairs with the method of upper bound analysis. J Mater Process Technol 209(5):2270–2276CrossRefGoogle Scholar
  8. 8.
    Intarakumthornchai T, Aue-U-Lan Y, Kesvarakul R, Jirathearanat S (2015) Feasible pressure and axial feed path determination for fuel filler tube hydroforming by genetic algorithm. Proc Inst Mech Eng B J Eng Manuf 229(4):623–630CrossRefGoogle Scholar
  9. 9.
    Yong Z, Chan LC, Chunguang W, Pei W (2009) Optimization for loading paths of tube hydroforming using a hybrid method. Mater Manuf Process 24(6):700–708CrossRefGoogle Scholar
  10. 10.
    Kesvarakul R, Intarakumthornchai T, Jirathearanat S, Aue-U-Lan Y Feasible pressure and axial feeding path determination of fuel filler by genetic algorithm (GA)Google Scholar
  11. 11.
    Ghosh A, Deshmukh K, Ngaile G (2011) Database for real-time loading path prediction for tube hydroforming using multidimensional cubic spline interpolation. J Mater Process Technol 211(1):150–166CrossRefGoogle Scholar
  12. 12.
    Aydemir A, De Vree J, Brekelmans W, Geers M, Sillekens W, Werkhoven R (2005) An adaptive simulation approach designed for tube hydroforming processes. J Mater Process Technol 159(3):303–310CrossRefGoogle Scholar
  13. 13.
    Doege E, Kosters R, Ropers C Determination of optimised control parameters for internal high pressure forming processes with the FEM. In: Proc. of the Int. Conf. on Sheet Metal, 1998Google Scholar
  14. 14.
    Strano M, Jirathearanat S, Altan T (2001) Adaptive FEM simulation for tube hydroforming: a geometry-based approach for wrinkle detection. CIRP Annals-Manufacturing Technology 50(1):185–190CrossRefGoogle Scholar
  15. 15.
    Ray P, Mac Donald B (2004) Determination of the optimal load path for tube hydroforming processes using a fuzzy load control algorithm and finite element analysis. Finite Elem Anal Des 41(2):173–192CrossRefGoogle Scholar
  16. 16.
    Teng B, Li K, Yuan S (2013) Optimization of loading path in hydroforming T-shape using fuzzy control algorithm. Int J Adv Manuf Technol 69(5–8):1079–1086CrossRefGoogle Scholar
  17. 17.
    Öztürk E, Türköz M, Halkacı HS, Koç M (2017) Determination of optimal loading profiles in hydromechanical deep drawing process using integrated adaptive finite element analysis and fuzzy control approach. Int J Adv Manuf Technol 88(9–12):2443–2459CrossRefGoogle Scholar
  18. 18.
    Yaghoobi A, Bakhshi-Jooybari M, Gorji A, Baseri H (2016) Application of adaptive neuro fuzzy inference system and genetic algorithm for pressure path optimization in sheet hydroforming process. Int J Adv Manuf Technol 86(9–12):2667–2677CrossRefGoogle Scholar
  19. 19.
    Hwang Y-M, Wang K-H, Kang N-S (2015) Adaptive simulations in T-shape tube hydroforming with different outlet diameters. Proc Inst Mech Eng B J Eng Manuf 229(4):597–608CrossRefGoogle Scholar
  20. 20.
    Sadegh yazdi M, Bakhshi-jooybari M, Gorji A, Shakeri M, Khademi M (2017) Investigation of forming cylindrical parts in a modified hydrodynamic deep drawing assisted by radial pressure with inward flowing liquid. J Manuf Sci Eng 140:031007. CrossRefGoogle Scholar
  21. 21.
    Gale WF, Totemeier TC (2003) Smithells metals reference book. Butterworth-HeinemannGoogle Scholar
  22. 22.
    Zhang S-H, Jensen MR, Danckert J, Nielsen KB, Kang D, Lang L (2000) Analysis of the hydromechanical deep drawing of cylindrical cups. J Mater Process Technol 103(3):367–373CrossRefGoogle Scholar
  23. 23.
    Hibbitt K (2013) ABAQUS: user’s manual: version 6.13. Hibbitt. Karlsson & Sorensen, Incorporated, ProvidenceGoogle Scholar
  24. 24.
    Bagherzadeh S, Mirnia M, Dariani BM (2015) Numerical and experimental investigations of hydro-mechanical deep drawing process of laminated aluminum/steel sheets. J Manuf Process 18:131–140CrossRefGoogle Scholar
  25. 25.
    Poor HZ, Moosavi H (2013) An investigation of wrinkling and thinning in hydroforming deep drawing process with hemispherical punch. Int J Mech Syst Eng 3(2):89–96Google Scholar
  26. 26.
    Habibi M, Hashemi R, Ghazanfari A, Naghdabadi R, Assempour A (2016) Forming limit diagrams by including the M–K model in finite element simulation considering the effect of bending. Proc Inst Mech Eng, Part L: J Mater: Des Appl 1464420716642258:146442071664225Google Scholar
  27. 27.
    Wisselink H, Nagy G, Meinders V Application of wrinkling criterion for prediction of side-wall wrinkles in deepdrawing of conical cups. In, 2012. IDDRG,Google Scholar
  28. 28.
    Anarestani SS, Morovvati M, Vaghasloo YA (2015) Influence of anisotropy and lubrication on wrinkling of circular plates using bifurcation theory. Int J Mater Form 8(3):439–454CrossRefGoogle Scholar
  29. 29.
    Li S-h, Yang B, W-g Z, Z-q L (2008) Loading path prediction for tube hydroforming process using a fuzzy control strategy. Mater Des 29(6):1110–1116CrossRefGoogle Scholar
  30. 30.
    Abbasi M, Ketabchi M, Labudde T, Prahl U, Bleck W (2012) New attempt to wrinkling behavior analysis of tailor welded blanks during the deep drawing process. Mater Des 40:407–414CrossRefGoogle Scholar
  31. 31.
    Shafaat MA, Abbasi M, Ketabchi M (2011) Effect of different yield criteria on prediction of wrinkling initiation of interstitial-free galvanized steel sheet. Mater Des 32(6):3370–3376CrossRefGoogle Scholar
  32. 32.
    Wang X, Cao J (2000) On the prediction of side-wall wrinkling in sheet metal forming processes. Int J Mech Sci 42(12):2369–2394CrossRefzbMATHGoogle Scholar
  33. 33.
    Dutilly M, Gelin J Design of sheet metal forming processes based on quality functions. In: Proceedings of the second international ESAFORM conference on material forming, 1999; pp 469–472Google Scholar
  34. 34.
    Banabic D (2000) Formability of metallic materials: plastic anisotropy, formability testing, forming limits. Springer Science & Business Media, BerlinCrossRefGoogle Scholar
  35. 35.
    Xing H, Makinouchi A (2001) Numerical analysis and design for tubular hydroforming. Int J Mech Sci 43(4):1009–1026CrossRefzbMATHGoogle Scholar
  36. 36.
    Hashemi A, Gollo MH, Seyedkashi SH (2016) Bimetal cup hydroforming of Al/St and Cu/St composites: adaptive finite element analysis and experimental study. J Mech Sci Technol 30(5):2217–2224CrossRefGoogle Scholar
  37. 37.
    Keum Y, Lee K (2000) Sectional finite element analysis of forming processes for aluminum-alloy sheet metals. Int J Mech Sci 42(10):1911–1933CrossRefzbMATHGoogle Scholar
  38. 38.
    Wang L-X (1999) A course in fuzzy systems. Prentice-Hall press, USAGoogle Scholar
  39. 39.
    Acharjya DP, Kauser AP (2015) Swarm intelligence in solving bio-inspired computing problems: reviews, perspectives, and challenges. Handbook of Research on Swarm Intelligence in Engineering:74–98Google Scholar
  40. 40.
    Karaboga D (2005) An idea based on honey bee swarm for numerical optimization. Technical report-tr06, Erciyes university, engineering faculty, computer engineering department,Google Scholar
  41. 41.
    Yazdi M, Latifi Rostami S, Kolahdooz A (2016) Optimization of geometrical parameters in a specific composite lattice structure using neural networks and ABC algorithm. J Mech Sci Technol 30(4):1763–1771CrossRefGoogle Scholar
  42. 42.
    Yan Y, Zhang Y, Gao F Dynamic artificial bee colony algorithm for multi-parameters optimization of support vector machine-based soft-margin classifier. EURASIP J Adv Signal Process 2012, 2012(1):160Google Scholar
  43. 43.
    Karaboga D, Akay B (2009) A comparative study of artificial bee colony algorithm. Appl Math Comput 214(1):108–132MathSciNetzbMATHGoogle Scholar
  44. 44.
    Zhu G, Kwong S (2010) Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl Math Comput 217(7):3166–3173MathSciNetzbMATHGoogle Scholar
  45. 45.
    Bacanin N, Tuba M (2012) Artificial bee colony (ABC) algorithm for constrained optimization improved with genetic operators. Stud Inform Control 21(2):137–146CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Milad Sadegh-yazdi
    • 1
  • Mohammad Bakhshi-Jooybari
    • 1
  • Mohsen Shakeri
    • 2
  • Hamid Gorji
    • 1
  • Maziar Khademi
    • 1
  1. 1.Advanced Material Forming Research Center, Department of Mechanical EngineeringBabol Noshirvani University of TechnologyBabolIran
  2. 2.Fuel Cell Research Center, Department of Mechanical EngineeringBabol Noshirvani University of TechnologyBabolIran

Personalised recommendations