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

Provenance of Source Code

Dominique Carrouge,
Phase Transformations Group,
Department of Materials Science and Metallurgy,
University of Cambridge,
Cambridge CB2 3QZ, U.K.

E-mail: dc249@cus.cam.ac.uk

Added to MAP: September 2002.

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A program for the prediction of the Ac1 temperature of steels, including martensitic stainless steels, as a function of chemical composition and heating rate.

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Product form:

Source Code (Unix only)

Operating System : Tested on Solaris, Linux and PC.

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MAP_STEEL_AC1TEMPcontains the programs which enable the user to estimate the Ac1 temperature of steels, including martensitic stainless steels, as a function of chemical composition and heating rate. It makes use of a neural network program called generate44, which was developed by David Mackay and is part of the bigback5 program. Eighty three experimental data relevant to 9-13 wt% Cr steels were added to the original database from Gavard et al work [1] and the updated database "database_ac1_martensitic.txt " is also provided . Following a Gaussian process analysis by Bailer-Jones et al [2], the elements considered to affect the Ac1 temperature are C, Si, Mn, Cu, Ni, Cr, Mo, Nb, V, W, Co. The model presented here combines the output of two different models which had five hidden units and different initial weights. The programs automatically calculate the result of each model and average those results to produce a prediction of Ac1 temperature and an error estimate.

A Linux/Unix and a PC version of the program are provided. The source code is accompanied by a program to install the program, which should run on most version of UNIX. Once uncompressed, and once the 'install' program runs (sh install), the directory contains


A text file containing the minimum and maximum limits of each input and output variable. This file is used to normalise and unnormalise the input and output data.


The input file that needs to be edited to make predictions. The variables should be entered in line, separated by one space and in the following order : C, Si, Mn, Cu, Ni, Cr, Mo, Nb, V, W, Co, Heating Rate.


This is a UNIX shell file containing the commands required to run the model. It can be executed by typing `sh model.gen' at the command prompt. This shell file normalises the input data, executes the neural network program, unnormalises the results and combine them to produce the final committee result.


Hidden executable file, to normalise the input data.


Hidden executable file, for the neural network program. It reads the normalised input file and also uses a configuration file spec.t generated by .generate\_spec and the weight files, located in the subdirectory c.


Hidden executable file, generates the configuration file read by .generate44.


Hidden executable file, it combines the output of the different models in a committee result.


Unnormalise the results.




The weight files of the different models.


Files containing information for calculating the size of the error bars for the different models.


Files containing information about the perceived significance value [3] for each model.


Files containing information about the noise, test error and log predictive error [3] for each model.




A normalised output file which was created during the building of the model. It is accessed by .generate44.



out1, out2, etc.

The normalised output files for each model.


The normalised output file containing the committee results. It is generated by .gencom .


To make predictions with the PC version: alter the test.dat file and double click on model.exe. The results are in the file Model_result.dat.


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  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, 12, (1996), 453-463.
  2. C. A. L. Bailer-Jones, H. K. D. H Bhadeshia and D. J. C. Mackay. Gaussian Process Modelling of Austenite Formation in Steel. Materials Science and technology, 15, 287-294, 1999
  3. D. J. C. Mackay, Probable Networks and Plausible Predictions - A Review of Practical Bayesian Methods For Supervised Neural Networks (1995). Web site : http://wol.inference.phy.cam.ac.uk/mackay/BayesNets.html.
  4. Review on neural networks in materials science

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Input parameters

The input variables are C, Si, Mn, Cu, Ni, Cr, Mo, Nb, V, W, Co, Heating Rate. The maximum and minimum values for each variable are given in the file MINMAX.

Output parameters

The output is written in the file result in the Unix/Linux version and in Model_RESULT.dat for the PC version. This file contains the predicted Ac1 temperature (in degree Celsius), the error bar value, the predicted value - the error and the predicted value + the error.

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Error Indicators

A full calculation of the error bars are given in reference [3].

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No information supplied.

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Further Comments


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1. Program text

Complete program.

2. Program data

"C" "Si" "Mn" "Cu" "Ni"	"Cr" "Mo" "Nb" "V" "W" "Co" "Heating Rate"
0.005	0.26	0.46	0	6.46	12.2 	2.48	0	0	0	0		1		
0.01	0.26	0.46	0	6.46	12.2 	2.48	0	0	0	0		1		
0.015	0.26	0.46	0	6.46	12.2 	2.48	0	0	0	0		1		
0.02	0.26	0.46	0	6.46	12.2 	2.48	0	0	0	0		1	
0.025	0.26	0.46	0	6.46	12.2 	2.48	0	0	0	0		1		
0.03	0.26	0.46	0	6.46	12.2 	2.48	0	0	0	0		1	

3. Program results

599.620728 27.860588 571.760193 627.481323
600.653503 28.078754 572.574768 628.732239
601.681885 28.325672 573.356201 630.007568
602.705444 28.600571 574.104858 631.306030
603.724976 28.901979 574.822998 632.626953
604.739319 29.228968 575.510376 633.968262

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Neural networks, Ac1 temperature, heating rate, martensitic stainless steels, carbon steels.

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Download PC source code as zip file

Download LINUX/UNIX source code as tar.gz file

Download database