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Incorporating neural network material models within finite element analysis for rheological behavior prediction
Incorporating neural network material models within finite element analysis for rheological behavior prediction
- Source :
- Journal of Pressure Vessel Technology. Feb, 2007, Vol. 129 Issue 1, p58, 8 p.
- Publication Year :
- 2007
-
Abstract
- The accuracy of a finite element model for design and analysis of a metal forging operation is limited by the incorporated material model's ability to predict deformation behavior over a wide range of operating conditions. Current theological models prove deficient in several respects due to the difficulty in establishing complicated relations between many parameters. More recently, artificial neural networks (ANN) have been suggested as an effective means to overcome these difficulties. To this end, a robust ANN with the ability to determine flow stresses based on strain, strain rate, and temperature is developed and linked with finite element code. Comparisons of this novel method with conventional means are carried out to demonstrate the advantages of this approach. [DOI: 10.1115/1.2389004]
Details
- Language :
- English
- ISSN :
- 00949930
- Volume :
- 129
- Issue :
- 1
- Database :
- Gale General OneFile
- Journal :
- Journal of Pressure Vessel Technology
- Publication Type :
- Academic Journal
- Accession number :
- edsgcl.160030210