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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2019> 28(3): 606-616
Junsheng Zhao, Yuantong Gu, Zhigang Feng. Optimization of Processing Parameters of Power Spinning for Bushing Based on Neural Network and Genetic Algorithms[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2019, 28(3): 606-616. doi: 10.15918/j.jbit1004-0579.18063
Citation: Junsheng Zhao, Yuantong Gu, Zhigang Feng. Optimization of Processing Parameters of Power Spinning for Bushing Based on Neural Network and Genetic Algorithms[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2019, 28(3): 606-616.doi:10.15918/j.jbit1004-0579.18063
shu

Optimization of Processing Parameters of Power Spinning for Bushing Based on Neural Network and Genetic Algorithms

doi:10.15918/j.jbit1004-0579.18063
  • 1.

    School of Mechanical Engineering, North University of China, Taiyuan 030051, China

  • 2.

    School of Chemistry, Physics and Mechanical Engineering, Queensland University of Technology, Brisbane, QLD 4001, Australia

  • Received Date:2018-06-30
    Abstract
  • A neural network model of key process parameters and forming quality is developed based on training samples which are obtained from the orthogonal experiment and the finite element numerical simulation. Optimization of the process parameters is conducted using the genetic algorithm (GA). The experimental results have shown that a surface model of the neural network can describe the nonlinear implicit relationship between the parameters of the power spinning process:the wall margin and amount of expansion. It has been found that the process of determining spinning technological parameters can be accelerated using the optimization method developed based on the BP neural network and the genetic algorithm used for the process parameters of power spinning formation. It is undoubtedly beneficial towards engineering applications.
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