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Provenance of Source Code

S Yoshitake (Mitsubishi Materials) and V Narayan,
Phase Transformations Group,
Department of Materials Science and Metallurgy,
University of Cambridge,
Cambridge, U.K.

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Provides data necessary to create a neural network model to predict lattice mismatch in nickel-based superalloys.

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Superalloys based on nickel have superior performance at high temperatures (e.g around 1000°C) and may be used to to the manufacture of high-performance creep-resistant turbine blades.

The standard heat treatment of nickel superalloys results in the formation of Ni3Al cuboidal precipitates (gamma-prime phase) in the nickel alloy matrix of fcc structure (gamma phase). The superior hgh temperature performance of the superalloy is attributed to the formation of these gamma-prime phase structures, which can form more than 60% by volume of the material [1]. During service, the crystal structure may undergo anisotropic coarsening, where the gamma-prime phase precipitate is 'rafted' in various directions [2]; the gamma-prime phase precipitates that have grown in the tensile direction hinder the movement of creep dislocations and thus contribute to creep performance.

The nickel-aluminium system is the simplest nickel-based superalloy. In addition, the alloy performance may be enhanced by alloying with elements which substitute for nickel and/or aluminium atomic sites and thus change change the lattice mismatch at the gamma/gamma-prime interfaces [3]. The activation energy of rafting has been found to be related to the lattice misfit of the two phases [4], defined by the equation:-

delta = -(alpha(gamma-prime)-alpha(gamma)) / alpha(gamma)

where alpha(gamma-prime) and alpha(gamma) are the lattice constants of the gamma-prime and gamma phases respectively.

A negative misfit has been found, experimentally, to be favourable for creep resistance [5,6,7,8]. We may predict, using a neural network model, which alloying elements favour a negative misfit, so that new alloys may be designed at minimal cost.

The database used with the neural network consists of:-

Two alloy groups are used:-

The TAR file lattmisfit.tar contains five files. Nickel.misfit.info is an ASCII text version of this document; the other four files make up the neural network database as follows:-

The data in Nickel.misfit.data.ga consist of 17 columns as follows:-

Column 1 is the temperature at which the experiment was conducted, in °C.

Columns 2-17 are compositions (in weight%) of the following elements:-

The data in Nickel.misfit.data.gpa consists of 15 column as follows:-

Column 1 is the temperature at which the experiment was conducted, in °C.

Columns 2-15 are compositions (in weight%) of the following elements:-

The data in Nickel.misfit.data.gp and Nickel.misfit.data.gpb consist of 1 column, giving the is the lattice constant in Ångstroms for the gamma and gamm-prime phases respectively of each alloy.

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  1. R.W. Guard and J.H. Westbrook, Trans Metal AIME, 215, 807 (1959).
  2. J.K. Tien and S.M. Copley, Metall. Trans. A, 2, 215 (1971).
  3. M. Enomoto and H. Harada, Metall. Trans. A, 20, 649 (1989).
  4. A. Pineau, Acta Metall., 24, 449 (1976).
  5. D.A. Grose and G.S. Ansell, Metall. Trans. A, 12, 1631 (1981).
  6. R.C. Ecob, R.A. Ricks and A.J. Porter, Scipta Metall., 16, 1085 (1982).
  7. A. Fedholm and J.L. Strudel, Superalloys, 211, (1984).
  8. D.F Larhman, R.D. Field, R. Dariola and H.L. Fraser, Acta Metall., 36, 1309 (1988).
  9. H. Harada, T. Yamagata, S. Nakazawa, K. Ohno and M. Yamazuki, Proc of Conference on High Temperature Materials for Power Engineering, Liege, Belgium, 1319, (1990).
  10. W.B. Pearson, A Handbook of Lattice Spacing and Structures of Metals and Alloys, 1-2, Pergamon Press, Oxford (1958)
  11. S. Ochai, S. Oyama and T. Suzuki, Acta Metall., 32, 289 (1984).
  12. S. Ochai, S. Oyama, T. Suzuki and P.M.E. Bull, 15, 53 (1984).

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Further Comments


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materials, data, neural, network, lattice, mismatch, nickel, superalloy, alloy, lattice parameter

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