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
Link To Document :
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