Design of a genetic algorithm to preform optimization for hot forging processes

Abstract

To this day, the design of preforms for hot forging processes is still a manual trial and error process and therefore time consuming. Furthermore, its quality vastly depends on the engineer’s experience. At the same time, the preform is the most influencing stage for the final forging result. To overcome the dependency on the engineer’s experience and time-consuming optimization processes this paper presents and evaluates a preform optimization by an algorithm for cross wedge rolled preforms. This algorithm takes the mass distribution of the final part, the preform volume, the shape complexity, the appearance of folds in the final part and the occurring amount of flash into account. This forms a multi-criteria optimization problem resulting in large search spaces. Therefore, an evolutionary algorithm is introduced. The developed algorithm is tested with the help of a connecting rod to estimate the influence of the algorithm parameters. It is found that the developed algorithm is capable of creating a suitable preform for the given criteria in less than a minute. Furthermore, two of the five given algorithm parameters, the selection pressure und the population size, have significant influence on the optimization duration and quality.

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Acknowledgements

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. The authors declare that they have no conflict of interest.

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Correspondence to Daniel Kampen.

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Kampen, D., Richter, J., Blohm, T. et al. Design of a genetic algorithm to preform optimization for hot forging processes. Int J Mater Form 13, 77–89 (2020). https://doi.org/10.1007/s12289-019-01469-4

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Keywords

  • Preform
  • Optimization
  • Genetic algorithm
  • Cross wedge rolled
  • Adaptive flash