Skip to main content
Springer Nature Link
Log in

Preform optimization for hot forging processes using an adaptive amount of flash based on the cross section shape complexity

  • Production Process
  • Published:
Production Engineering Aims and scope Submit manuscript

Abstract

In multistage hot forging processes, the preform shape is the parameter mainly influencing the final forging result. Nevertheless, the design of multistage hot forging processes is still a trial and error process and therefore time consuming. The quality of developed forging sequences strongly depends on the engineer’s experience. To overcome these obstacles this paper presents an algorithm for solving the multi-objective optimization problem in designing preforms. Cross wedge rolled preforms were chosen as subject of investigation. An evolutionary algorithm is introduced to optimize the preform shape taking into account the mass distribution of the final part, the preform volume and the shape complexity. A crucial factor in preform optimization for hot forging processes is the amount of flash. Therefore an equation for improving the amount of flash is derived. The developed algorithm is tested using two connecting rods with different shape complexities as demonstration parts.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Behrens B-A, Nickel R, Müller S (2009) Flashless precision forging of a two-cylinder crankshaft. Prod Eng Res Dev 3:381–389. doi:10.1007/s11740-009-0185-x

    Article  Google Scholar 

  2. Guan Y et al (2014) Preform design in forging process based on quasi-quipotential field and response surface methods. Procedia Eng 81:468–473. doi:10.1016/j.proeng.2014.10.024

    Article  Google Scholar 

  3. Li Q, Lovell M (2008) Cross wedge rolling failure mechanisms and industrial application. Int J Adv Manuf Technol 37(3–4):265–278. doi:10.1007/s00170-007-0979-y

    Article  Google Scholar 

  4. Pater Z, Tomczak J (2012) Experimental tests for cross wedge rolling of forgings made from non-ferrous metal alloys. Arch Metall Mater 4:919–928. doi:10.2478/v10172-012-0101-9

    Google Scholar 

  5. Kache H, Stonis M, Behrens B-A (2012) Development of a warm cross wedge rolling process using FEA and downsized experimental trials. Prod Eng Res Dev 6:339–348. doi:10.1007/s11740-012-0379-5

    Article  Google Scholar 

  6. Li Q et al (2002) Investigation of the morphology of internal defects in cross wedge rolling. J Mater Process Technol 125–126:248–257. doi:10.1016/S0924-0136(02)00303-5

    Article  Google Scholar 

  7. Behrens B-A et al (2013) Basic study on the process combination of deposition welding and subsequent hot bulk forming. Prod Eng Res Dev 7(6):585–591

    Article  Google Scholar 

  8. Blohm T, Stonis M, Behrens B-A (2015) Investigation of simulation parameters for cross wedge rolling titanium and bainitic grade steel. Appl Mech Mater 736:165–170

    Article  Google Scholar 

  9. Cakircali M, Kilicaslan C, Güden M, Kiranli E, Shchukin V, Petronko V (2013) Cross wedge rolling of a Ti6Al4 V (ELI) alloy: the experimental studies and the finite element simulation of the deformation and failure. Int J Adv Manuf Technol 65:1273–1287. doi:10.1007/s00170-012-4256-3

    Article  Google Scholar 

  10. Neugebauer R, Lorenz B, Steger J (2009) Cross wedge rolling to preform light metal forgings. Met Matter 12:13

  11. Tofil A, Tomczak J, Pater Z (2013) Cross wedge rolling with upsetting. Arch Metall Mater 58:1191–1196. doi:10.2478/amm-2013-0150

    Google Scholar 

  12. Bartnicki J, Pater Z, Gontarz A, Tomczak J (2014) Innovative metal forming technologies. J Mach Eng 14:5–16

    Google Scholar 

  13. Meyer M, Stonis M, Behrens B-A (2015) Cross wedge rolling and bi-directional forging of preforms for crankshafts. Prod Eng Res Dev 9:61–71. doi:10.1007/s11740-014-0581-8

    Article  Google Scholar 

  14. Hatzenbichler T, Buchmayr B (2008) Vorformoptimierung für das Gesenkschmieden mittels numerischer Simulation. BHM Berg-Huettenmaenn Monatsh 153:413–417. doi:10.1007/s00501-008-0422-1

    Article  Google Scholar 

  15. Mirsaeidi M et al (2009) Optimum forging preform shape design by interpolation of boundary nodes. In: Proceedings of the world congress on engineering, vol 2

  16. Behrens B-A, Nickel R, Stonis M (2012) Simulation algorithm for the assessment and modification of multi-directional forging processes and tool gemoetries. Prod Eng Res Dev 6:187–198. doi:10.1007/s11740-012-0364-z

    Article  Google Scholar 

  17. Shao Y, Lu B, Ou H, Ren F, Chen J (2014) Evolutionary forging preform design optimization using strain-based criterion. Int J Adv Manuf Technol 71:69–80. doi:10.1007/s00170-013-5456-1

    Article  Google Scholar 

  18. Ciancio C, Citrea T, Ambrogio G, Filice L, Musmanno R (2015) Design of a high performance predictive tool for forging operation. Procedia CIRP 33:173–178. doi:10.1016/j.procir.2015.06.032

    Article  Google Scholar 

  19. Landkammer P et al (2016) A non-invasive form finding method with application to metal forming. Prod Eng Res Dev 10:93–102. doi:10.1007/s11740-016-0659-6

    Article  Google Scholar 

  20. Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, London

    MATH  Google Scholar 

  21. Haupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley, Hoboken

    MATH  Google Scholar 

Download references

Acknowledgments

The authors thank the German Research Foundation (Deutsche Forschungsgemeinschaft) for the funding of the research project “Entwurf optimaler Vorformstufen zum Herstellen von Schmiedebauteilen unter Anwendung von stochastischen Optimierungsverfahren” (DFG Be 1691/177-1 and DFG OV 36/22-1). The authors thank the Hammerwerk Fridingen GmbH for the support in this project.

Author information

Authors and Affiliations

  1. Institut für Integrierte Produktion Hannover, Hollerithallee 6, 30419, Hannover, Germany

    Johannes Knust, Malte Stonis & Bernd-Arno Behrens

Authors
  1. Johannes Knust
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Malte Stonis
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Bernd-Arno Behrens
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Johannes Knust.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Knust, J., Stonis, M. & Behrens, BA. Preform optimization for hot forging processes using an adaptive amount of flash based on the cross section shape complexity. Prod. Eng. Res. Devel. 10, 587–598 (2016). https://doi.org/10.1007/s11740-016-0702-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11740-016-0702-7

Keywords