The optimum method on injection molding condition based on RBF network and ant colony algorithm

  • Authors:

  • Fengli Huang;Jinmei Gu;Jinhong Xu

  • Affiliations:

  • School of Mechanical and Electrical Engineering, Jiaxing university, Jiaxing, Falkand Isl;School of Mechanical and Electrical Engineering, Jiaxing university, Jiaxing, China;School of Mechanical and Electrical Engineering, Jiaxing university, Jiaxing, China

  • Venue:
  • Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
  • Year:
  • 2009

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Abstract

Aiming at the two principal; quality factors (warpage quantity and shrinkage rate) in injection molding process, the optimum method on injection molding condition based on RBF network and ant colony algorithm is provided. The definition and calculation method of excellent degree are given first and then the optimum method of the approximate model based on radial basis neural network is given. In the case study of plastic injection of fruit plate, the range of molding condition and the design method of design variables based on excellent degree are given, then the approximate model is gotten by Hyper-Latin square experiment and RBF network, the optimum result is gotten by improved ant colony algorithm of continuous field. It shows that the optimum result of plastic injection parameters based on radial basis neural network response surface and ant colony algorithm is reliable, and has good practical meaning.