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Design and optimization of turbine blade preform forging using RSM and NSGA II

Author

Listed:
  • S. H. R. Torabi

    (University of Tehran)

  • S. Alibabaei

    (University of Tehran)

  • B. Barooghi Bonab

    (Shahid Rajaee University)

  • M. H. Sadeghi

    (Tarbiat Modares University)

  • Gh. Faraji

    (University of Tehran)

Abstract

Forging is one of the production methods of turbine blades. But, because of the complexities of the blades, they cannot be produced in one stage and using preforms is necessary. In this paper, an extruded elliptical cross section was considered as blade preform, then response surface method and multi-objective genetic algorithm was used to optimize this preform. Maximum filling ratio of the final die and minimum flash volume, forging force and strain variance of final blade were considered as objectives of optimization. Design Expert software was used for design of experiment and optimization. Also Deform-3D software was applied to simulate the forging process. The optimized preform was compared with the preform resulted from conventional preform designing method. Results show that optimization method gives better results than conventional method. Also physical modeling was used for verification of simulation results. Results show simulation results have a good corresponding with experimental results.

Suggested Citation

  • S. H. R. Torabi & S. Alibabaei & B. Barooghi Bonab & M. H. Sadeghi & Gh. Faraji, 2017. "Design and optimization of turbine blade preform forging using RSM and NSGA II," Journal of Intelligent Manufacturing, Springer, vol. 28(6), pages 1409-1419, August.
    • Handle: RePEc:spr:joinma:v:28:y:2017:i:6:d:10.1007_s10845-015-1058-0
    DOI: 10.1007/s10845-015-1058-0
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    Cited by:

    1. Kai Meng & Xiaoming Qian & Peihuang Lou & Jiong Zhang, 2020. "Smart recovery decision-making of used industrial equipment for sustainable manufacturing: belt lifter case study," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 183-197, January.
    2. Liang Hou & Roger J. Jiao, 2020. "Data-informed inverse design by product usage information: a review, framework and outlook," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 529-552, March.

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