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The limit drawing ratio in die angled hydromechanical deep drawing method

  • Hasan BallikayaEmail author
  • Vedat Savas
  • Cetin Ozay
ORIGINAL ARTICLE
  • 52 Downloads

Abstract

In the study, the effect of die angle, die radius, die punch, blank holder force, and chamber pressure parameters on the limit drawing ratio (LDR) in die angled hydromechanical deep drawing process was investigated by using Taguchi experimental design method. The effects of the parameters on the limit drawing ratio were numerically examined and compared experimentally using ANSYS packaged software. Also, the data were statistically analyzed by using the ANOVA analysis of variance in order to determine the effects of the parameters on the limit drawing ratio. The results indicated that the chamber pressure value was lower and the limit drawing ratio was higher than the values reported in the literature.

Keywords

Die angled hydromechanical deep drawing Taguchi Anova ANSYS 

Notes

References

  1. 1.
    Zhang SH, Danckert J (1998) Development of hydro-mechanical deep drawing. J Mater Process Technol 83:14–25CrossRefGoogle Scholar
  2. 2.
    Zhang SH, Nielsen KB, Danckert J, Kang DC, Lang LH (2000) Finite element analysis of the hydromechanical deep-drawing process of tapered rectangular boxes. J Mater Process Technol 102:1–8CrossRefGoogle Scholar
  3. 3.
    Lang L, Li T, An D, Chi C, Nielsen KB, Danckert J (2009) Investigation into hydromechanical deep drawing of aluminum alloy complicated components in aircraft manufacturing. Mater Sci Eng 499:320–324CrossRefGoogle Scholar
  4. 4.
    Thiruvarudchelvan S, Tan MJ (2006) A note on fluid-pressure-assisted deep drawing processes. J Mater Process Technol 172:174–181CrossRefGoogle Scholar
  5. 5.
    Danckert J, Nielsen KB (2000) Hydromechanical deep drawing with uniform pressure on the flange. CIRP Ann 49:217–220CrossRefGoogle Scholar
  6. 6.
    Lang L, Danckert J, Nielsen KB (2004) Investigation into hydrodynamic deep drawing assisted by radial pressure part ı. experimental observations of the forming process of aluminum alloy. J Mater Process Technol 148:119–131CrossRefGoogle Scholar
  7. 7.
    Taghipour E, Assempour A (2011) The effects of proportional loading, plane stress, and constant thickness assumptions on hydro-mechanical deep drawing process. Int J Mech Sci 53:329–337CrossRefGoogle Scholar
  8. 8.
    Liu X, Xu Y, Yuan S (2008) Effects of loading paths on hydrodynamic deep drawing with ındependent radial hydraulic pressure of aluminum alloy based on numerical simulation. J Mater Sci Technol Shenyang 24:395–399Google Scholar
  9. 9.
    Yong-chao X, Xin L, Xiao-jing L, Shi-jian Y (2008) Deformation and defects in hydroforming of 5A06 aluminum alloy dome with controllable radial pressure. J Cent S Univ Technol 16:887–891Google Scholar
  10. 10.
    Savas V, Secgin Ö (2007) A new type of deep drawing die design and experimental results. Mater Des 28:1330–1333CrossRefGoogle Scholar
  11. 11.
    Savas V, Ozay C, Aytac F (2015) The experimental investigation of drawing parameters on the deep drawing of Al1050 sheets in angular deep-drawing dies. Optoelectron Adv Mater Rapid Commun 9:230–233Google Scholar
  12. 12.
    Özek C, Ünal E (2011) The effect of die/blank holder angles on limit drawing ratio and wall thickness ın deep drawing of square cups. J Fac Eng Archit Gazi Univ 27:615–622Google Scholar
  13. 13.
    Karaağaç İ, Özdemir A (2011) The formability of erdemir 6112 sheet metal by the hydromechanical deep drawing process. J Fac Eng Archit Gazi Univ 26:841–850Google Scholar
  14. 14.
    Canıyılmaz E (2003) An alternative approach to analysis of variance in taguchi method. J Fac Eng Archit Gazi Univ 18:51–63Google Scholar
  15. 15.
    Raju S, Ganesan G, Karthıkeyan R (2010) Influence of variables in deep drawing of AA 6061 sheet. Trans Nonferrous Metals Soc China 20:1856–1862CrossRefGoogle Scholar
  16. 16.
    Altuğ M (2016) Investigation of material removal rate (MRR) and wire wear ratio (WWR) for alloy Ti6Al4 V exposed to heat treatment processing in WEDM and optimization of parameters using Grey relational analysis. Mater Test 58:794–804CrossRefGoogle Scholar
  17. 17.
    Altuğ M, Erdem M, Özay C, Bozkır O (2016) Surface roughness of Ti6Al4V after heat treatment evaluated by artificial neural networks. Mater Test 58:189–199CrossRefGoogle Scholar
  18. 18.
    Zareh B, Gorji AH, Bakhshi M, Nourouzi S (2013) Study on the effect of forming parameters in sheet hydrodynamic deep drawing using FEM-based Taguchi method. Int J Adv Des Manuf Technol 6:87–99Google Scholar
  19. 19.
    Padmanabhana R, Oliveiraa MC, Alvesb JL, Menezesa LF (2007) Influence of process parameters on the deep drawing of stainless steel. Finite Elem Anal Des 43:1062–1067CrossRefGoogle Scholar
  20. 20.
    Colgan M, Monaghan J (2003) Deep drawing process: analysis and experiment. J Mater Process Technol 132:35–41CrossRefGoogle Scholar
  21. 21.
    Srinivas T, Reddy AC (2015) Parametric optimization of warm deep drawing process of 1100 aluminum alloy: validation through FEA. Int J Sci Eng Res 6:425–433Google Scholar
  22. 22.
    Tschaetsch H (2006) Metal forming practice. Springer-Verlag, Berlin, pp 172–184Google Scholar
  23. 23.
    Ozek C, Bal M (2009) The effect of die/blank holder and punch radiuses on limit drawin ratio in angular deep-drawing dies. Int J Adv Manuf Technol 40:1077–1083CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Machine and Metal Technologies, Malatya Organized İndustrial Zone Vocational High SchoolInonu UniversityMalatyaTurkey
  2. 2.Firat University Faculty of Technology Mechanical Engineering Department ElazigTurkey

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