Volume 35 Issue 10
Oct.  2009
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Chen Xingkai, Xie Huaiqin. Optimization for CFRP pultrusion process based on genetic algorithm-neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(10): 1263-1267. (in Chinese)
Citation: Chen Xingkai, Xie Huaiqin. Optimization for CFRP pultrusion process based on genetic algorithm-neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(10): 1263-1267. (in Chinese)

Optimization for CFRP pultrusion process based on genetic algorithm-neural network

  • Received Date: 10 Aug 2008
  • Publish Date: 31 Oct 2009
  • According to the curing kinetics and heat transfer theory, the models of unsteady temperature field and resin curing for the pultrusion process of carbon fiber reinforced polymer (CFRP) were established. The finite element/finite different method associated with the indirect decoupling method based on ANSYS to simulate the temperature and degree of cure for CFRP pultrusion. The fiber Bragg gratings (FBG) sensor, encapsulated in an aluminum capillary, was utilized to real time monitor the temperature during pultrusion; and the final degree of cure was measured by Sorbitic extraction. The results show that the numerical model is reliable and correct. With the simulative results, the neural network was trained and predicted the degree of cure effectively. On the basis of the trained network, the genetic algorithm was used to optimize the temperature of die. It shows that the optimization method is effective and convenient.

     

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