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.
Issue Section:
Research Papers
1.
Prassad
, Y. V. R. K.
, Sasidhara
, S.
, Gegel
, H. L.
, and Malas
, J. C.
, 1997, Hot Working Guide: A Compendium of Processing Maps
, ASM International
, pp. 104
–105
.2.
Lange
, K.
, 1985, Handbook of Metal Forming
, McGraw-Hill
, New York
, p. 3.21
.3.
Sellars
, C. M.
, and Tegart
, W. J. McG.
, 1963, Mem. Sci. Rev. Metall.
0025-9128, 63
, p. 86
.4.
Laasroui
, A.
, and Jonas
, J. J.
, 1966, “Prediction of Steel Flow Stresses at High Temperatures and Strain Rates
,” Metall. Mater. Trans. A
1073-5623, 22
, pp. 1545
–1558
.5.
Brown
, S. B.
, Kim
, K. H.
, and Anand
, L.
, 1989, “An Internal Variable Constitutive Model for Hot Working of Metals
,” Int. J. Plast.
0749-6419, 5
, pp. 95
–130
.6.
“
Standard Test Methods of Compression Testing Metallic Materials at Room Temperature
,” ASTM E9-89a, Annual Book of ASTM Standards.7.
“
Standard Practice for Compression Tests of Metallic Materials at Elevated Temperatures with Conventional or Rapid Heating Rates and Strain Rates
,” ASTM E209-00, Annual Book of ASTM Standards.8.
Zhao
, D.
, 2000, “Testing for Deformation Modeling
,” ASM Handbook
, ASM International
, Vol. 8
, pp. 798
–809
.9.
Rao
, K. P.
, and Prasad
, Y.
, 1990, “Neural Network Approach to Flow Stress Evaluation in Hot Deformation
,” J. Mater. Process. Technol.
0924-0136, 53
, pp. 552
–566
.10.
Hodgson
, P. D.
, Kong
, L. X.
, and Davies
, C.
, 1999, “The Prediction of the Hot Strength in Steels with an Integrated Phenomenological and Artificial Neural Network Model
,” J. Mater. Process. Technol.
0924-0136, 87
, pp. 131
–138
.11.
Enemuoh
, E. U.
, and El-gizawy
, A. S.
, 2002, “A Robust Neural Network Model for Online Prediction of Process-Induced Damage in Drilling Epoxy Composites
,” Proceedings 3rd CIRP Int. Sem. on Intelligent Computation in Mfg. Eng.
, Naples, Italy, pp. 183
–188
.12.
El-Gizawy
, A. S.
, Hwang
, J. Y.
, and Brewer
, D. H.
, 1990, “A Strategy for Integrating product and Process Design of Aerospace Components
,” Manufacturing Review
, ASME
, 3
, pp. 178
–186
.13.
Bariani
, P. F.
, Bruschi
, S.
, and Negro
, T. D.
, 2002, “Neural Network to Describe Superalloys Rheological behavior Under Hot Working Conditions
,” Proceedings 3rd CIRP Int. Sem. on Intelligent Computation in Mfg. Eng.
, Naples, Italy, pp. 585
–588
.14.
Fausett
, L.
, 2002, Fundamentals of Neural Networks, Architectures, Algorithms, and Applications
, Prentice-Hall, Englwood Cliffs, NH, p. 13
.15.
McCullock
, W.
, and Pitts
, W.
, 1943, “A Logical Calculus of the Ideas Immanent in Nervous Activity
,” Bulletin of Mathematical Biophysics
, 5
, pp. 115
–133
.16.
Hagan
, M. T.
, Demuth
, H. B.
, and Beale
, M.
, 1996, Neural Network Design
, PWS Publishing
, pp. 12
–19
.17.
Jeffreys
, H.
, 1939, Theory of Probability
, Oxford University Press
, Oxford
.18.
Gull
, S. F.
, 1988, “Bayesian Inductive Inference and Maximum Entropy
,” Maximum Entropy and Bayesian Methods in Science and Engineering, Vol. 1: Foundations
, G. J.
Erickson
and C. R.
Smith
, eds., Kluwer
, Dordrecht
, pp. 53
–74
.19.
MacKay
, D. J. C.
, 1992, “Bayesian Interpolation
,” Neural Computation
, Massachusetts Institute of Technology
, 4
, pp. 415
–447
.Copyright © 2007
by American Society of Mechanical Engineers
You do not currently have access to this content.
