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Integrative process chain optimization using a Genetic Algorithm

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Abstract

For the production of forged components, it is necessary to coordinate and optimize the production stages along the process chain. This includes the mainstream processes as well as the associated process chain of the die manufacturing. Up to now, these processes and process chains are planned and optimized independent from each other because of the different and often contradictory target criteria. In this paper, a new approach for a holistic optimization of forged process chains will be presented. At first, a systematic mathematical dependency-analysis between the processes of an application scenario was carried out. Based on this analysis, a holistic Pareto-based optimization of the process parameters by the use of a Genetic Algorithm was consecutively performed. The article ends with the presentation and discussion of the computational results.

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References

  1. Mehnen J, Michelitsch T, Bartz-Beielstein T, Henkenjohann N (2004) Systematic analyses of multi-objective evolutionary algorithms applied to real-world problems using statistical design of experiments. Intelligent computation in manufacturing engineering, 4th CIRP international seminar on intelligent computation in manufacturing engineering

  2. Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading

    MATH  Google Scholar 

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

    MATH  Google Scholar 

  4. Saravanan R (2006) Manufacturing optimization through intelligent techniques. CRC Taylor & Francis, Boca Raton

    Google Scholar 

  5. Garen J (2003) SGMGA: Self-guided multi-objective genetic algorithm. In: Proceedings of the fifth metaheuristics international conference, Kyoto

  6. Yang SH, Natarajan U (2010) Multi-objective optimization of cutting parameters in turning process using differential evolution and non-dominated sorting genetic algorithm-II approaches. Int J Adv Manuf Technol 49:773–784

    Article  Google Scholar 

  7. Denkena B, Rudzio H, Brandes A (2006) Methodology for dimensioning technological interfaces of manufacturing process chains, CIRP Annals. vol 55/1

  8. Tönshoff HK, Denkena B, Friemuth T, Zwick M, Brandes A (2002) Technological interfaces of industrial process chains. Annals of the German academic society for production engineering (WGP)

  9. Hensel A, Spittel T (1978) Kraft- und Arbeitsbedarf bildsamer Formgebungsverfahren. Deutscher Verlag für Grundstoffindustrie, Leipzig

    Google Scholar 

  10. Bobke T (1991) Randschichtphänomene bei Verschleißvorgängen an Gesenkschmiedewerkzeugen, Dissertation Universität Hannover

  11. Schliephake U (1993) Analyse des Werkzeugverschleißes beim Gesenkschmieden, Dissertation Leibniz Universität Hannover

  12. German Institute for Standardization e. V. (2001) DIN EN ISO 4957—Tool steels, Beuth Verlag, Berlin

  13. Spur G, Stöferler T (1987) Handbuch der Fertigungstechnik–Wärmebehandeln. Carl Hanser Verlag, München

    Google Scholar 

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Acknowledgments

The authors thank the German Research Foundation (DFG) for its financial support of the project “Integrative Prozesskettenplanung und -auslgegung umformtechnisch gefertigter Bauteile auf Basis genetischer Algorithmen” with the project numbers DE 447/68-1 and BE 1691/92-1.

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Authors and Affiliations

  1. Institute of Production Engineering and Machine Tools, Leibniz Universität Hannover, An der Universität 2, 30823, Garbsen, Germany

    B. Denkena & F. Charlin

  2. Institute of Metal Forming and Metal-Forming Machines, Leibniz Universität Hannover, An der Universität 2, 30823, Garbsen, Germany

    B.-A. Behrens & M. Dannenberg

Authors
  1. B. Denkena
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  2. B.-A. Behrens
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  3. F. Charlin
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  4. M. Dannenberg
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Corresponding authors

Correspondence to F. Charlin or M. Dannenberg.

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Denkena, B., Behrens, BA., Charlin, F. et al. Integrative process chain optimization using a Genetic Algorithm. Prod. Eng. Res. Devel. 6, 29–37 (2012). https://doi.org/10.1007/s11740-011-0347-5

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  • DOI: https://doi.org/10.1007/s11740-011-0347-5

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