Advertisement

Production Engineering

, Volume 12, Issue 3–4, pp 419–429 | Cite as

Development of a predictive simulation method for thin flash generation in flashless precision forging processes of aluminum parts using FEA and experiments

  • Johannes RichterEmail author
  • Malte Stonis
  • Jan Langner
  • Thoms Blohm
  • Bernd-Arno Behrens
Production Process

Abstract

In this paper, the investigation of thin flash generation in precision forging process of an aluminum long flat part is described. The aim was to derive a predictive simulation method for thin flash generation in order to increase both process and part quality in the future. The forging processes were varied by use of different preforms with equal volumes but different mass distributions while using the same final part geometry. The experimentally forged parts were analyzed concerning the amount and part area of the generated thin flash. The conducted FE simulations were analyzed concerning the hydrostatic pressure values p in the part areas near to the tool gap between upper and lower die immediately before form-filling. For a more detailed comparison, single p values were included to hydrostatic pressure functions P. The comparison between the P functions and the experimentally determined thin flash height shows, that high pressure values as well as high gradients of the P functions indicate less thin flash generation. The method therefore allows a qualitative prediction of thin flash generation. It can provide two kind of information. First: The prediction of the specific locations where thin flash is likely to occur in one final part by use of one single preform. Second: The qualitative prediction of the specific final part areas were thin flash is likely to occur depending on different preform geometries. This method will decreases the necessity of time-consuming forging trials and can shorten the preform designing process in the future.

Keywords

Forging Flashless precision forging FEA Aluminum Predictive simulation method 

Abbreviations

Tb

Billet temperature

vf

Forming velocity/press speed

wg

Width of gap

T

Temperature

ε

Effective strain

σf

Flow stress

\(\dot {\varepsilon }\)

Stain rate

A

Solidity

m1, m9

Coefficient for dependence of the temperature

m2

Coefficient for dependence of strain hardening

m3

Coefficient for dependence of equivalent strain rate

m4

Coefficient for dependence of equivalent strain

m5

Coefficient for term coupling temperature and stress

m7

Coefficient for term sensitivity of material to stress

m8

Coefficient for term coupling temperature and stress rate

τR

Friction stress

σN

Normal stress

µ

Coefficient of friction

m

Factor of friction

k

Shear yield strength

Tdie

Initial die temperature

Ta

Ambient temperature

U

Thermal effusivity

αT

Heat transfer coefficient

p

Hydrostatic pressure

P

Hydrostatic pressure function.

l

Length of the measuring line

Notes

Acknowledgements

The Research Project “ProGrAl” (STO 1011/4 − 1) has been funded by the German Research Foundation (DFG). The authors would like to thank the German Research Foundation (DFG) for the financial and organizational support of this Project. The authors declare that they have no conflict of interest.

References

  1. 1.
    Behrens B-A, Suchmann P, Schott A (2008) Warm forging: new forming sequence for the manufacturing of long flat pieces, Production Engineering, Research and Development, vol 2, no 3. Springer, Berlin 3, 381–389Google Scholar
  2. 2.
    Doege E (2018) Mehrfachwirkende Stempelwerkzeuge (DO 190/119-3), (2004) report German Research Foundation (DFG)Google Scholar
  3. 3.
    Siegert K, Ringhand D (1994) Flashless and precision forging of connecting rods from P/M aluminum alloys. J Mater Process Technol 46:157–167CrossRefGoogle Scholar
  4. 4.
    Kim S-Y, Tsuruoka K, Yamamoto T (2014) Effect of forming speed in precision forging process evaluated using CAE technology and high performance servo-press machine. Procedia Eng 81:2415–2420CrossRefGoogle Scholar
  5. 5.
    Bin Z et al (2015) Design of relief-cavity in closed-precision forging of gears, J. Cent. South Univ., vol 20, no 4. Central South University Press and Springer-Verlag, Berlin, Heidelberg, 1287–1297Google Scholar
  6. 6.
    Zhang Y, Jian S, Zha Y, Shan D (2013) Isothermal precision forging of complex–shape rotating disk of aluminum alloy based on processing map and digitized technology. Mater Sci Eng A 580:294–304CrossRefGoogle Scholar
  7. 7.
    Farhoumand A, Ebrahimi R (2009) Analysis of forward–backward-radial extrusion process. Mater Design 30:2152–2157CrossRefGoogle Scholar
  8. 8.
    Langner J, Stonis M, Behrens B-A (2015) Experimental investigation of a variable flash gap regarding material flow and influence of trigger forces, Production Engineering, Research and Development, vol 9, no 3. Springer, New York, 289–297Google Scholar
  9. 9.
    Richter J et al (2017) Analysis of an aluminum forging process in completely enclosed dies considering the numerical predicition of thin flash generation in small gaps. J Mech Sci Technol 31(7):3429–3435CrossRefGoogle Scholar
  10. 10.
    Richter J et al (2017) Quality optimization for aluminum precision forging processes in completely enclosed dies of long forging parts by prediction and avoidance of thin flash generation. Procedia Eng Proc ICTP 207:484–489CrossRefGoogle Scholar
  11. 11.
    Hensel A, Spittel T (1978) Kraft- und Arbeitsbedarf bildsamer Formgebungsverfahren, Deutscher Verlag für GrundstoffindustrieGoogle Scholar
  12. 12.
    Altan T, Vazquez V (1996) Numerical process simulation for tool and process design in bulk metal forming. CIRP Ann Manuf Technol 45(2):599–615CrossRefGoogle Scholar
  13. 13.
    Behrens B-A et al (2015) Advanced friction modeling for bulk metal forming processes. Prod Eng Res Dev 5(6):621–627CrossRefGoogle Scholar
  14. 14.
    Springorum F (2017) Gegenwart und Zukunft der Massivumformung, Conference proceedings 22. Umformtechnisches Kolloquium Hannover, editor.: Prof. Dr.-Ing. Behrens, PZH Verlag, pp 1–15Google Scholar

Copyright information

© German Academic Society for Production Engineering (WGP) 2018

Authors and Affiliations

  • Johannes Richter
    • 2
    Email author
  • Malte Stonis
    • 2
  • Jan Langner
    • 2
  • Thoms Blohm
    • 2
  • Bernd-Arno Behrens
    • 1
  1. 1.Institute of Forming Technology and Machines (IFUM)Leibniz Universität HannoverGarbsenGermany
  2. 2.Institut für Integrierte Produktion Hannover gemeinnützige GmbH (IPH)HanoverGermany

Personalised recommendations