Incorporating neural network material models within finite element analysis for rheological behavior prediction

Journal of Pressure Vessel Technology, Transactions of the ASME

Volumn 129, Issue 1, 2007, Pages 58-65

  Scott Kessler, B ^a   El Gizawy, A Sherif ^a   Smith, Douglas E ^a  

^a Kestek   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

RHEOLOGICAL BEHAVIOR PREDICTION; RHEOLOGICAL MODELS;

FINITE ELEMENT METHOD; MATHEMATICAL MODELS; NEURAL NETWORKS; PLASTIC FLOW; RHEOLOGY; STRAIN RATE;

FORGING;

EID: 34248198965     PISSN: 00949930     EISSN: None     Source Type: Journal    
DOI: 10.1115/1.2389004     Document Type: Article

References (19)

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., 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, 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., 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., 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., 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 Pills, 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.