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Materials Algorithms Project
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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

S. J. P. Longworth
MPhil in Materials Modelling,
Department of Materials Science and Metallurgy,
University of Cambridge,
Cambridge CB2 3QZ, U.K.

Added to MAP: March 2001.

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A program for the estimation of the elongation (%) of mild steels as a function of elemental composition, heat and mechanical treatments and grain size.

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Language: FORTRAN / C
Product form: Executable files

Operating System: Solaris 5.5.1 & Linux

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The modelling procedure is a purely empirical one, and is based on a neural network program called generate44, which was developed by David MacKay and is part of the bigback5 program. The model is constituted of a committee of several individual neural networks. It was trained on a set of experimental data for which the "outputs" are known, and creates a kind of non-linear, multi-parameter "regression" of the outputs versus the inputs. This "regression" has already been produced and the model is delivered ready to perform predictions for steels of any desired composition (within certain specified limits). The source code for the neural network program can be downloaded from David MacKay's website; the executable files only are available from MAP.

The program runs on a Solaris 5.1.1 unix operating system and Linux. The files for unix are separated compressed into a file called Steel_Elong_unix.tar.gz or Steel_Elong_linux.tar.gz  ;The .tar.gz file contains the following files:

A manual containing step-by-step instructions for running the program, including a list of input variables.
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.
An input text file containing the input variables used for predictions, together with an example set of data.
Contains the results you should expect from the example set of data. To test the model is running properly on your computer, use the given 'test.dat' file to do predictions and compare the 'result' file with this file.
This is a unix shell file containing the command steps required to run the module. It can be executed by typing sh model.gen  at the command prompt. These shell files run all the programs necessary for normalising the input data, executing the network for each model, unnormalising the output data and combining the results of each model to produce the final committee result.
Created by generate_spec, which contains information about the module and the number of data items being supplied. It is read by the program generate44.
.generate_spec (hidden)
This executable file creates a file called spec.t1, required by generate44.
.randomise (hidden)
This executable file creates a file called norm_test.in, which contains the normalised equivalent of the input data found in test.dat. It requires the MINMAX file
This is the executable file for the neural network program. It reads the normalised input data file, norm_test.in (created by normalise) , and uses the weight files in subdirectory c, to find a value for the output. The results are written to the temporary output file _out.
This executable file combines the predictions of the different models in the committee and calculates the combined error bar.
This executable un-normalise the committee predictions and produces the file 'result'.
Contains the final un-normalised committee results for the predicted output.
The weights files for 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 for each model.
Files containing values for the noise, test error and log predictive error for each model.
A normalised output file which was created during the building of the model. It is accessed by generate44 via spec.t1.
The normalised output file containing the committee results. It is generated by .gencom.

Detailed instructions on the use of the program are given in the README file.

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  1. D. J. C. MacKay, Bayesian non-linear modelling with neural networks, University of Cambridge programme for industry: modelling phase transformations in stels, 1995. [Download PDF file]

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Input parameters

C (wt.%)

Si (wt.%)

Mn (wt.%)

P (wt.%)

S (wt.%)

Cr (wt.%)

Mo (wt.%)

Ni (wt.%)

Al (wt.%)

B (wt.%)

Cu (wt.%)

N (wt.%)

Nb (wt.%)

Sn (wt.%)

Ti (wt.%)

V (wt.%)

First heat treatment (high temperature): duration (min) and temperature (oC)

1st Second heat treatment: duration (min) and temperature (oC)

2nd Third heat treatment: duration (min) and temperature (oC)

Roll finish temperature (o)

Rolling reduction (%)

Rolled (binary)

Austenitised (binary)

Normalised (binary)

Tempered (binary)

As rolled (binary)

Spheroidised (binary)

Cold rolled (binary)

Control rolled (binary)

Soaked (binary)

Annealed (binary)

Cold reduction after hot roll (binary)

Aged (binary)

Warm rolled (binary)

Twice normalised (binary)

Cold drawn (binary)

Cooling rate (oCs-1 - all specimens assumed to be of equal section)

MLI (mm)


Output parameters

predicted reduction of area (%)

error bar on reduction of area

reduction of area - error bar

reduction of area + error bar

A more detailed description is presented in the README file.

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Error Indicators


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An estimated predictive error bar is provided by the model.

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Further Comments

This neural network model was created as part of a group project for the MPhil in Materials Modelling at the University of Cambridge. The author would like to thank Professor H.D.K.H. Bhadeshia, Dr. L. Greer and T. Sourmail for their invaluable assistance.

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1. Download the model

Uncompress the "Steel_Elong_unix.tar.gz" (or "Steel_Elong_linux.tar.gz") file in a dedicated directory (for example: "neural").
On UNIX systems, this is done by:

2. Program data

3. Running the program (making predictions)

For Solaris 5.5.1 or Linux, just type:

sh model.gen

4. Results of the program (predictions)

The results are written in the "Result" or "model_result.dat" file, as described in the README file. In the present case:

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Auxiliary Routines

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neural networks, steel, reduction of area, ductility

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Download package (Solaris 5.5.1) (10 Mb)
Download package (Linux) (10 Mb)

Download package (IRIX) (10 Mb)

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MAP originated from a joint project of the National Physical Laboratory and the University of Cambridge.

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