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New approach for the optimization of pass-schedules in open-die forging

  • Martin WolfgartenEmail author
  • Dirk Rosenstock
  • Fridtjof Rudolph
  • Gerhard Hirt
Original Research
  • 77 Downloads

Abstract

Open-die forging is an incremental forming process, which is mainly used for the production of large parts with high requirements regarding the mechanical properties and reliability of the forged parts. Finite element analysis (FEA) is able to simulate open die forging sequences. It is therefore very suitable to confirm, whether a selected schedule will be successful in terms of reaching the desired geometry and internal product quality. However, it is comparably slow and therefore not suitable for early process design, when out of an almost infinite number of potential sequences of strokes, an appropriate pass schedule needs to be designed. This is today usually achieved by pass schedule planning software, which takes into account volume constancy, empirical spread behavior and average temperature evolution. However, they do not account for product quality characteristics like microstructure and voids closure. In this paper recently developed fast models, which are able to calculate the temperature, equivalent strain and microstructure evolution along the core fibre of a forged workpiece are coupled with an optimization algorithm to allow automatic pass schedule layout and optimization. Different cost functions are evaluated regarding their impact on the resulting properties of the workpiece. The results indicate that for an overall optimization of open-die forging processes different phenomena and influencing parameters need to be considered, since all of these parameters have a significant influence on the resulting properties such as equivalent strain, temperature and grain size of the ingot.

Keywords

Open-die forging Pass schedule calculation Fast modelling Optimization 

Nomenclature

εh

Height reduction

\( {s}_{B_0}/{h}_0 \)

Bite ratio

d

Austenitic grain size

ϑF

Furnace temperature

ϑmin

Min. ingot temp

sB

Bite width

B

Die width

εeq

Equivalent strain

si

Penalty term

ϑSurf

Surface temperature

ϑmax

Maximum allowed forming temperature

ni

Weight exponent

\( {\dot{\varepsilon}}_V \)

Equivalent strain rate

ϑ

(Temperature)

ci

Target term

\( {g}_{c_i} \)

Weight target term

\( \overline{\varepsilon} \)

Mean equivalent strain

\( {s}_{\overline{\varepsilon}} \), \( {s}_{\overline{d}} \)

Corresponding Standard deviation

\( \overline{d} \)

Mean austenitic grain size

tS

Process time

\( {g}_{S_i} \)

Weight penalty term

Fi

Forging force in step i

Fmax

Max. press force

qtot

Quality of optimization result

Notes

Acknowledgements

The author would like to thank the Deutsche Forschungsgemeinschaft DFG for the support of this research within the Cluster of Excellence EXC 128 “Integrative Production Technology for High-Wage Countries” and EXC 2023 “Internet of Production”

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Tomlinson A, Stringer JD (1959) Spread and elongation in flat tool forging. Journal of the Iron and Steel Institute 193(2):157–162Google Scholar
  2. 2.
    Knap M, Kugler G, Palkowski H, Turk R (2004) Prediction of material spreading in hot open-die forging. Steel Research International 75(6):405–410CrossRefGoogle Scholar
  3. 3.
    Fister W, Schmidt R (1986) Computer-aided program forging for axissymetric hammer forging. Steel and Iron 106(22):1213–1218Google Scholar
  4. 4.
    Nieschwitz P, Ecken FP, Siemer E (1988) Pass schedule calculation program „Comforge“ for open-die forging plants. MPT- Metallurgical Plant and Technology 11(5): 54–68Google Scholar
  5. 5.
    Schmitz W (2003) Forgebase- a tool for optimization of the open-die forging process. International Forgemasters Meeting. Kobe, Japan, pp 398–402Google Scholar
  6. 6.
    Kim PH, Chun MS, Yi JJ, Moon YH (2002) Pass schedule algorithms for hot open die forging. J Mater Process Technol 130-131:516–532CrossRefGoogle Scholar
  7. 7.
    Siemer E, Nieschwitz P, Kopp R (1986) Quality-optimized process control in open-die forging. Stahleisen: pp. 383–387Google Scholar
  8. 8.
    Recker D, Franzke M, Hirt G, Rech R, Steingießer K (2010) Grain size prediction during open die forging processes. La Metallurgia Italiana 9:29–35Google Scholar
  9. 9.
    Recker D, Franzke M, Hirt G (2011) Fast models for online-optimization during open die forging. CIRP annals- manufacturing. Technology 60:295–298Google Scholar
  10. 10.
    Rosenstock D, Recker D, Franzke M, Hirt G, Sommler D, Steingießer K-J, Tewes A, Rech R, Gehrmann B, Kirchhoff S, Lamm R (2014) Online visualization during open die forging and optimization of pass schedules. Steel Research International 85(9):1348–1354CrossRefGoogle Scholar
  11. 11.
    Rosenstock D (2018) Schnelle Prozessmodellierung, Online-Visualisierung und Optimierung beim Freiformschmieden. Dissertation, RWTH Aachen UniversityGoogle Scholar
  12. 12.
    Karhausen K, Kopp R (1992) Model for integrated process and microstructure simulation in hot forming. Steel Research 63(6):247–256CrossRefGoogle Scholar
  13. 13.
    Blaes N, Bokelmann D, Poppenhäger J, Wagner H (1997) Optimizing of the forging process of heavy forgings for power generation machinery. International Forgemasters Meeting: Advances in Heavy Forgings, Pusan, Korea: 93–102Google Scholar
  14. 14.
    Recker D, Rosenstock D, Franzke M, Hirt G (2012) Optimisation of a basic pass schedule for open die forging with regards to a homogeneous strain distribution along the core. In: 1st International Conference on Ingot Casting Rolling and Forging (ICRF): 1–7Google Scholar
  15. 15.
    Banaszek G, Szota P (2005) A comprehensive numerical analysis of the effect of relative feed during the operation of stretch forging of large ingots in profiled anvils. J Mater Process Technol 169(3):437–444CrossRefGoogle Scholar
  16. 16.
    Aivazi R, Yanagimoto J (2004) Automated sequence design for slab stretching with arbitrary height distribution using one-step FEM analysis. J Mater Process Technol 151(1–3):146–154CrossRefGoogle Scholar
  17. 17.
    Frazier WG, Seshacharyulu T, Medeiros RC, Prasad Y (2002) Control of transient thermal response during sequential open-die forging: a trajectory optimization approach. J Manuf Sci Eng 124(3):502–508CrossRefGoogle Scholar
  18. 18.
    Wolfgarten M, Rosenstock D, Schaeffer L, Hirt G (2015) Implementation of an open-die forging process for large hollow shafts for wind power plants with respect to an optimized microstructure. La Metallurgia Italiana. 4:43–49Google Scholar
  19. 19.
    Hook R, Jeeves TA (1961) “Direct search” solution of numerical and statistical problems. J ACM 8(2):212–229CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag France SAS, part of Springer Nature 2019

Authors and Affiliations

  • Martin Wolfgarten
    • 1
    Email author
  • Dirk Rosenstock
    • 1
  • Fridtjof Rudolph
    • 1
  • Gerhard Hirt
    • 1
  1. 1.IBF - Institute of Metal Forming, RWTH AachenAachenGermany

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