Preform optimization for hot forging processes using genetic algorithms

  • Johannes Knust
  • Florian Podszus
  • Malte Stonis
  • Bernd-Arno Behrens
  • Ludger Overmeyer
  • Georg Ullmann
ORIGINAL ARTICLE

Abstract

In multi-stage hot forging processes, the preform shape is the parameter mainly influencing the final forging result. Nevertheless, the design of multi-stage 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 when designing preforms. Cross-wedge-rolled (CWR) 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. The developed algorithm is tested using a connecting rod as a demonstration part. Based on finite element analysis, the implemented fitness function is evaluated, and thus the progressive optimization can be traced.

Keywords

Preforming optimization Hot forging Evolutionary algorithms Cross-wedge rolling 

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References

  1. 1.
    Behrens B-A, Nickel R, Müller S (2009) Flashless precision forging of a two-cylinder crankshaft. Prod Eng 3:381–389. doi: 10.1007/s11740-009-0185-x CrossRefGoogle Scholar
  2. 2.
    Guan Y et al. (2014) Preform design in forging process based on quasi-quipotential field and response surface methods. Procedia engineering 81:468–473. doi: 10.1016/j.proeng.2014.10.024 CrossRefGoogle Scholar
  3. 3.
    Lange K (1988) Umformtechnik Band 2: Massivumformung. Springer-Verlag, Berlin u.aGoogle Scholar
  4. 4.
    Hatzenbichler T, Buchmayr B (2008) Vorformoptimierung für das Gesenkschmieden mittels numerischer simulation. BHM Berg- und Hüttenmännische Monatshefte 153:413–417. doi: 10.1007/s00501-008-0422-1 CrossRefGoogle Scholar
  5. 5.
    Mirsaeidi M et al (2009) Optimum forging preform shape design by interpolation of boundary nodes. Proceedings of the World Congress on Engineering.Vol 2Google Scholar
  6. 6.
    Behrens B-A, Nickel R, Stonis M (2012) Simulation algorithm for the assessment and modification of multi-directional forging processes and tool geometries. Prod Eng Res Devel 6:187–198. doi: 10.1007/s11740-012-0364-z CrossRefGoogle Scholar
  7. 7.
    Shao Y, Lu B, Ou H, Ren F, Chen J (2014) Evolutionary forging preform design optimization using strain-based criterion. Int J AdvManuf Technol 71:69–80. doi: 10.1007/s00170-013-5456-1 CrossRefGoogle Scholar
  8. 8.
    Goldberg D (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, LondonMATHGoogle Scholar
  9. 9.
    Haupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley, HobokenMATHGoogle Scholar
  10. 10.
    Sedighi M, Hadi M, Kolahdouz S (2009) Optimization of preform in close die forging by combination of neural network and genetic algorithm. World Applied Sciences Jorunal 7(11):1464–1473Google Scholar
  11. 11.
    Torabi SHR et al. (2015) Design and optimization of turbine blade preform forging using RSM and NSGA II. J Intell Manuf. doi: 10.1007/s10845-015-1058-0 Google Scholar
  12. 12.
    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 CrossRefGoogle Scholar
  13. 13.
    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 Devel 6:339–348. doi: 10.1007/s11740-012-0379-5 CrossRefGoogle Scholar
  14. 14.
    Altan T, Vazquez V (1996) Numerical process simulation for tool and process design in bulk metal forming. CIRP Annals-Manufacturing Technology, Elsevier Ltd 45(2):599–615CrossRefGoogle Scholar
  15. 15.
    Behrens BA, Bouguecha A, Hadifi T, Mielke J (2015) Advanced friction modeling for bulk metal forming processes, production engineering, research and development. Springer Verlag Berlin 5(6):621–627Google Scholar
  16. 16.
    Klawitter G (2005) Werkstoffflusssteuerung beim Gesenkschmieden durch eine im Gesenkumlauf variierende Gratbahngeometrie. PZH Produktionstechn, ZentrumGoogle Scholar

Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • Johannes Knust
    • 1
  • Florian Podszus
    • 1
  • Malte Stonis
    • 1
  • Bernd-Arno Behrens
    • 1
  • Ludger Overmeyer
    • 1
  • Georg Ullmann
    • 1
  1. 1.Institut für Integrierte Produktion HannoverHannoverGermany

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