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 rheological 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.
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February 2007
Research Papers
Incorporating Neural Network Material Models Within Finite Element Analysis for Rheological Behavior Prediction
B. Scott Kessler,
B. Scott Kessler
Kestek
, 4602 W 121st Street, Suite 111, Overland Park, KS 66209
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A. Sherif El-Gizawy,
A. Sherif El-Gizawy
Kestek
, 4602 W 121st Street, Suite 111, Overland Park, KS 66209
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Douglas E. Smith
Douglas E. Smith
Kestek
, 4602 W 121st Street, Suite 111, Overland Park, KS 66209
Search for other works by this author on:
B. Scott Kessler
Kestek
, 4602 W 121st Street, Suite 111, Overland Park, KS 66209
A. Sherif El-Gizawy
Kestek
, 4602 W 121st Street, Suite 111, Overland Park, KS 66209
Douglas E. Smith
Kestek
, 4602 W 121st Street, Suite 111, Overland Park, KS 66209J. Pressure Vessel Technol. Feb 2007, 129(1): 58-65 (8 pages)
Published Online: February 25, 2006
Article history
Received:
September 12, 2005
Revised:
February 25, 2006
Citation
Kessler, B. S., El-Gizawy, A. S., and Smith, D. E. (February 25, 2006). "Incorporating Neural Network Material Models Within Finite Element Analysis for Rheological Behavior Prediction." ASME. J. Pressure Vessel Technol. February 2007; 129(1): 58–65. https://doi.org/10.1115/1.2389004
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