Modelling beta transus temperature of titanium alloys using artificial neural network

Computational Materials Science

Volumn 32, Issue 1, 2005, Pages 1-12

  Guo, Z ^a   Malinov, S ^a   Sha, W ^a  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

Author keywords

Beta transus temperature; Neural network; Titanium alloys

Indexed keywords

COMPUTER SIMULATION; DATABASE SYSTEMS; NEURAL NETWORKS; PARAMETER ESTIMATION; PROBABILITY DISTRIBUTIONS; PROBLEM SOLVING; REGRESSION ANALYSIS; TENSILE STRENGTH;

ARTIFICIAL NEURAL NETWORKS (ANN); BETA TRANSUS TEMPERATURE; CONTINUOUS-COOLING-TRANSFORMATION (CCT); INPUT PARAMETERS;

TITANIUM ALLOYS;

EID: 9744256988     PISSN: 09270256     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.commatsci.2004.05.004     Document Type: Article

References (32)

1. J.C. Williams, E.A. Starke, The role of thermomechanical processing in tailoring the properties of aluminum and titanium alloys, in: Krauss, G. (Ed.), Deformation, Processing, and Structure, American Society for Metals, Metals Park, OH, 1984, p. 317.
2. V.G. Krishna, Y.V.R.K. Prasad, N.C. Birla, G.S. Rao, Processing map for the hot working of near-α titanium alloy 685, Journal of Materials Processing Technology 71 (1997) 377-383.
3. C.F. Yolton, F.H. Froes, R.F. Malone, Alloying element effects in metastable beta titanium alloys, Metallurgical Transactions A 10A (1979) 132-134.
4. P.J. Bania, Beta titanium alloys and their role in the titanium industry, Journal of the Minerals, Metals and Material Society 46 (7) (1994) 16-19.
5. S. Ankem, G.K. Scarr, I.L. Caplan, J.C. Williams, S.R. Sergle, H.B. Bomberger, Multiple regression analysis of the effects of various alloying elements on the properties of titanium alloys, in: P. Lacombe, R. Tricot, G. Beranger (Eds.), Proceedings of 6th World Conference on Titanium, Cannes, France, June 6-9, 1988, vol. 3, Societe Francaise de Metallurgie, 1989, pp. 265-268.
6. H. Onodera, Y. Ro, T. Yamagata, M. Yamazaki, Design of titanium alloys, in: G. Lutjering, U. Zwicker, W. Bunk (Eds.), Titanium Science and Technology, Proceedings of 5th International Conference on Titanium, Munich, Germany, September 10-14, 1984, vol. 3, Deutsche Gesellschaft fur Metallkunde E.V. Oberursal, 1985, pp. 1883890.
7. R.L. Harvey, Neural Network Principles, Prentice Hall, Englewood Cliffs, NJ, 1994.
8. M. Smith, Neural Networks for Statistical Modelling, Van Nostrand Reinhold, New York, 1993.
9. W.G. Vermeulen, P.J.V. Wolk, A.P. Weijer, S.V. Zwaag, Prediction of jominy hardness profiles of steels using artificial neural networks, Journal of Materials Engineering and Performance 5 (1) (1996) 57-62.
10. H.K.D.H. Bhadeshia, Methodology of modelling and software, in: Cambridge Programme for Industry: Modelling Phase Transformations in Steels, Department of Materials Science and Metallurgy, St. Catharine's College, Cambridge, March 20-22, 1995.
11. T. Cool, H.K.D.H. Bhadeshia, D.J.C. MacKay, The yield and ultimate tensile strength of steel welds, Materials Science and Engineering A 223A (1997) 186-200.
12. A.Y. Badmos, H.K.D.H. Bhadeshia, D.J.C. MacKay, Tensile properties of mechanically alloyed oxide dispersion strengthened iron alloys: Part I - neural network models, Materials Science and Technology 14 (1998) 793-809.
13. S.B. Singh, H.K.D.H. Bhadeshia, Estimation of bainite plate-thickness in low-alloy steels, Materials Science and Engineering A 245A (1998) 72-79.
14. D.J.C. MacKay, Bayesian non-linear modelling with neural networks, in: Cambridge Programme for Industry: Modelling Phase Transformations in Steels, Department of Materials Science and Metallurgy, St. Catharine's College, Cambridge, March 20-22, 1995.
15. C. Dumortier, P. Lehert, P. Krupa, A. Charlier, Statistical modelling of mechanical properties of microalloyed steels by application of artificial neural networks, Materials Science Forum 284-286 (1998) 393-400.
16. L.X. Kong, P.D. Hodgson, D.C. Collinson, Modelling the effect of carbon content on hot strength of steels using a modified artificial neural network, ISIJ International 38 (10) (1998) 1121-1129.
17. P.J. Wolk, S. Zwaag, in: Workshop on Artificial Neural Networks, Laboratory of Materials Science, Delft University of Technology, Rotterdamseweg 137, 2628 AL Delft, The Netherlands.
18. S. Malinov, W. Sha, Z. Guo, Application of artificial neural network for prediction of time-temperature-transformation diagrams in titanium alloys, Materials Science and Engineering A 283A (1-2) (2000) 1-10.
19. S. Malinov, W. Sha, J.J. McKeown, Modelling the correlation between processing parameters and properties in titanium alloys using artificial neural network, Computational Materials Science 21 (2001) 375-394.
20. Z. Guo, W. Sha, Modelling of beta transus temperature in titanium alloys using thermodynamic calculation and neural networks, in: I.V. Gorynin, S.S. Ushkov (Eds.), Titanium 99: Science and Technology, Proceedings of 9th World Conference on Titanium, Saint Petersburg, Russia, June 7-11, 1999, vol. 1, Central Research Institute of Structural Materials, 2000, pp. 61-68.
21. Z. Guo, W. Sha, Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network, Computational Materials Science 29 (2004) 12-28.
22. N. Saunders, Modelling of phase equilibria in Ti-alloys, in: P.A. Blenkinsop, W.J. Evans, H.M. Flower (Eds.), Titanium'95: Science and Technology, Proceedings of 8th World Conference on Titanium, Birmingham, UK, October 22-26, 1995, vol. 3, The Institute of Materials, London, 1996, pp. 2167-2176.
23. K. Hornik, M. Stinchcombe, H. White, Multilayer feed-forward networks are universal approximators, Neural Networks 2 (5) (1989) 359-366.
24. R. Boyer, G. Welsh, E.W. Coolings (Eds.), Materials Properties Handbook: Titanium Alloys, The Materials Information Society, ASM International, Materials Park, OH, 1998 (1st printing in 1994).
25. A.F. Wilson, Private Communication, TIMET Ltd., P.O. Box 704, Witton, Birmingham B6 7UR, UK, 1999.
26. Titanium and its alloys: database compiled from data supplied by the member companies of Titanium Information Group and Titanium Development Association, developed by MATUS Databases Engineering Information Co. Ltd., 1992.
27. P.A. Blenkinsop, W.J. Evans, H.M. Flower (Eds.), Titanium '95: Science and Technology, Proceedings of 8th World Conference on Titanium, Birmingham, UK, October 22-26, 1995, The Institute of Materials, London, 1996.
28. R.R. Boyer, H.W. Rosenberg (Eds.), Beta Titanium Alloys in the 1980's, The Metallurgical Society of AIME, 1984.
29. E.K. Molchanova, Phase Diagrams of Titanium Alloys (Translation of Atlas Diagram Sostoyaniya Titanovyk Splavov), Israel Program for Scientific Translations, Jerusalem, 1965, p. 154.
30. The Math Works, Inc. product, Neural Network Toolbox Version 3 for MatLab, pp. 5_51-5_58, 6_1-6_20.
31. N. Cristianini, J. Shawe-Taylor, An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, Cambridge, 2000.
32. Y.H. Hu, J.N. Hwang (Eds.), Handbook of Neural Network Signal Processing, CRC Press, Boca Raton, FL, 2001.