• DocumentCode
    2781208
  • Title

    A constraint based evolutionary decision support system for product design

  • Author

    Guoyan, Yu ; Xiaozhen, Wang ; Peng, Li

  • Author_Institution
    Eng. Coll., Guangdong Ocean Univ., Zhanjiang, China
  • fYear
    2009
  • fDate
    17-19 June 2009
  • Firstpage
    2585
  • Lastpage
    2590
  • Abstract
    At conceptual design stage, designers often tend to be restricted by general stereotypes and by their previous design experiences. Consequently, this paper proposes a constraint-based cooperative interactive design method, in order to combine human intelligence and computer intelligence for product design. The model of constraint based human machine cooperative interactive design system is described, how to realize the human machine cooperative interactive optimal search by genetic algorithm (GA) is discussed in detail. In design process, GA is employed to search for a near-optimal design, and a trained neural network is used as a fitness function. Finally, a mould injection structure design is chosen as the subject of the current investigation.
  • Keywords
    CAD/CAM; decision support systems; genetic algorithms; injection moulding; learning (artificial intelligence); man-machine systems; moulding equipment; product design; search problems; constraint-based cooperative interactive design method; evolutionary decision support system; genetic algorithm; human intelligence; human machine cooperative interactive optimal search; mould injection structure design; neural network training; product design; Algorithm design and analysis; Competitive intelligence; Decision support systems; Design methodology; Genetic algorithms; Humans; Machine intelligence; Neural networks; Process design; Product design; Constraint based; Cooperative Interactive; Genetic algorithm; Product design;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference, 2009. CCDC '09. Chinese
  • Conference_Location
    Guilin
  • Print_ISBN
    978-1-4244-2722-2
  • Electronic_ISBN
    978-1-4244-2723-9
  • Type

    conf

  • DOI
    10.1109/CCDC.2009.5191831
  • Filename
    5191831