• DocumentCode
    3136561
  • Title

    Neural network method for inverse modeling of material deformation

  • Author

    Ivezic, Nenad ; Allen, J.D. ; Zacharia, Thomas

  • Author_Institution
    Div. of Comput. Sci. & Math., Oak Ridge Nat. Lab., TN, USA
  • Volume
    2
  • fYear
    1999
  • fDate
    1999
  • Firstpage
    961
  • Abstract
    A method is described for inverse modeling of material deformation in applications of importance to the sheet metal forming industry. The method was developed in order to assess the feasibility of utilizing empirical data in the early stages of the design process as an alternative to conventional prototyping methods. Because properly prepared and employed artificial neural networks (ANN) were known to be able to codify and generalize large bodies of empirical data, they were the natural choice for this application. The product of the work described here is a desktop ANN system that can produce in one pass an accurate die design for a user-specified part shape
  • Keywords
    CAD; deformation; digital simulation; forming processes; inverse problems; mechanical engineering computing; metallurgical industries; microcomputer applications; neural nets; accurate die design; artificial neural networks; desktop ANN system; inverse modeling; material deformation; sheet metal forming industry; Artificial intelligence; Artificial neural networks; Computer science; Geometry; Inorganic materials; Inverse problems; Neural networks; Prototypes; Shape; Sheet materials;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Processing and Manufacturing of Materials, 1999. IPMM '99. Proceedings of the Second International Conference on
  • Conference_Location
    Honolulu, HI
  • Print_ISBN
    0-7803-5489-3
  • Type

    conf

  • DOI
    10.1109/IPMM.1999.791512
  • Filename
    791512