Materials Algorithms Project
Program Library

Subroutine MAP_STEEL_AC3

1. Provenance of code.
2. Purpose of code.
3. Specification.
4. Description of subroutine's operation.
5. References.
6. Parameter descriptions.
7. Error indicators.
8. Accuracy estimate.
9. Any additional information.
10. Example of code
11. Auxiliary subroutines required.
12. Keywords.
13. Download source code.
14. Links.

Provenance of Source Code

Dr Shgh Woei Ooi, November 2006, steveooi@postech.ac.kr
Computational Metallurgy Group,
Graduate Institute of Ferrous Metallurgy (GIFT),
Pohang University of Science and Technology (POSTECH),
Republic of Korea

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Purpose

To predict the Ac₃ temperature of steel as a function of the chemical composition and the heating rate.

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Specification

Description

MAP_STEEL_AC3 uses neural network analysis to predict the temperature at the completion of austenite formation.

The neural net was trained using 394 of a database of 788 examples constructed using information from Refs [1-7]. The remaining 394 examples were used as `new' experiments to test the trained network. This database is in the file `neural_dataset'. The data are available in MAP_DATA_AUSTENMAT.

An excel file (Input and Copy.xls) containing the chemical composition and heating rate table is included. The user can edit the excel file, copy and paste special (in unformatted text) the composition and heating rate to test.dat file. The user needs to save the file before proceeding. To start the prediction (in the Windows operating system) double click on the file model.exe (the executable program).

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References

1. L. Gavard, H.K.D.H. Bhadeshia, D.J.C. MacKay, and S. Suzuki, Bayesian Neural Network Model for Austenite Formation in Steels, Materials Science and Technology, 1996, 12, 453-463.
2. G.F. Vander Vroot, ed., Atlas of Time-Temperature- Transformation Diagrams for Irons and Steels, ASM International, Ohio, USA, (1991).
3. Special Report 56, Atlas of Isothermal Transformation Diagrams of B.S. En Steels, 2nd edition, Iron and Steel Institute, London, (1956).
4. T. Cool, Systematic Design of Welding Alloys for Power Plant Steels, CPGS Thesis, University of Cambridge, (1994).
5. K. Akibo, Scientific American (Japanese Edition), (January 1993), 20-29.
6. R. Reed, Ph.D. Thesis, University of Cambridge, (1987).
7. Phase Transformation Kinetics and Hardenability of Medium Alloy Steels, Climax Molybdenum Company, Connecticut, USA, (1972).

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Parameters

2. Program inputs

Inputs in sequence C, Si, Mn, S, P, Cu, Ni, Cr, Mo, Nb, V, Ti, Al, B, W, As, Sn, Zr, Co, N, O (all in wt%) followed by heating rate in K/s. The following example has three sets of inputs.

    0.09    0.17    0.97    0       0       0.019   0.09    9.2     0.96    0.045   
0.18    0       0       0       0.97    0       0       0       1.87    0.052   
0       5

0.09    0.17    0.97    0       0       0.019   0.09    9.2     0.96    0.045   
0.18    0       0       0       0.97    0       0       0       1.87    0.052   
0       10

0.09    0.17    0.97    0       0       0.019   0.09    9.2     0.96    0.045   
0.18    0       0       0       0.97    0       0       0       1.87    0.052   
0       20

3. Program results

The output is the Ac3 temperature in centrigrade, followed by a one sigma modelling uncertainty, Ac3-uncertainty and Ac3+uncertainty. The outputs listed below correspond to the inputs described above.

  Ac3 / C  Error / C  Ac3-Error  Ac3+Error
899.464417 19.997770 879.466675 919.462158
911.400269 19.649918 891.750366 931.050171
933.057861 20.260756 912.797119 953.318604

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Auxiliary Routines

None.

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Keywords

neural network, austenite formation

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Download

Download source code (Windows)

Download source code (Unix)

Download source code (Linux)

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