[MAP Logo]

Materials Algorithms Project
Program Library


  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

Miguel Angel Yescas-Gonzalez and H.K.D.H. Bhadeshia,
Phase Transformations Group,
Department of Materials Science and Metallurgy,
University of Cambridge,
Cambridge, U.K.

The neural network program was produced by:

David MacKay,
Cavendish Laboratory,
University of Cambridge,
Madingley Road,
Cambridge, CB3 0HE, U.K.

Added to MAP: August 2000

Top | Next


Estimation of the tensile yield strength in ADI as a function of chemical composition
and heat treatment conditions (Austenitising temperature, austenitising time, austempering
temperature and austempering time).

Top | Next | Prev


Language: FORTRAN / C
Product form: Source code / Executable files
Operating Selntem: Solaris 5.5.1 & Windows 95/98 

Top | Next | Prev


MAP_NEURAL_ADI_ELONGATION  contains a suite of programs which enable the user to estimate the tensile elongation in % for any austempered ductile iron (ADI) as a function of chemical composition and heat treatment conditions. It makes use of a neural network program called generate44, which was developed by David MacKay and is part of the bigback5 program. The network was trained using a large database of experimental results [1].  12 different models are provided, which differ from each other by the number of hidden units and by the value of the seed used when training the network. It was found that a more accurate result could be obtained by averaging the results from all the models [2]. This suite of programs calculates the results of each model and then combines them, by averaging, to produce a committee result and error estimate, as described by MacKay [page 387 of reference 2]. The source code for the neural network program can be downloaded from David MacKay's website; the executable files only are available from MAP. Also provided are FORTRAN programs (as source code) for normalising the input data, averaging the results from the neural network program and unnormalising the final output file, along with other files necessary for running the program.

Programs are available which run on a Solaris 5.5.1 unix operating system, and on a PC under Windows 95/98. A set of program and data files are provided for the model, which calculate the tensile elongation in % for ADI. The files for UNIX are included in a directory called ADI. This directory contains the following files and subdirectories:

A text file 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.
This is a unix shell file containing the command steps required to run the module. It can be executed by typing csh model.gen  at the command prompt. This shell file compiles and runs 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.
This executable program for the PC correspond to the unix command file model.gen.
A dynamic file, created by spec.ex/spec.exe, which contains information about the module and the number of data items being supplied. It is read by the program generate44/generate55.exe.
This is a text file which contains the normalised input variables. It is generated by the program normtest.for in subdirectory s.
generate44 / generate55
This is the executable file for the neural network program. generate44 runs on unix operating system and generate55 on the PC. It reads the normalised input data file, norm_test.in, and uses the weight files in subdirectory c. The results are written to the temporary output file _out.
_ot, _out, _res, _sen
These files are created by generate44 and can be deleted.
Contains the final un-normalised committee results for the predicted hardness.
The source code for program spec.ex.
Program to normalise the data in test.dat and produce the normalised input file norm_test.in. It makes use of information read in from no_of_rows.dat and committee.dat.
This program uses the information in committee.dat and combines the predictions from the individual models, in subdirectory outprdt, to obtain an averaged value (committee prediction). The output (in normalised form) is written to com.dat.
Program to un-normalise the committee results in com.dat and write the output predictions to unnorm_com. This file is then renamed Result.
A text file containing the number of models to be used to form the committee result and the number of input variables. It is read by gencom.for, normtest.for and treatout.for.
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 [1] for each model.
Files containing values for the noise, test error and log predictive error [1] for each model.
A normalised output file which was created when developing the model. It is accessed by generate44 via spec.t1.
out1, out2 etc.
The normalised output files for each model.
The normalised output file containing the committee results. It is generated by gencom.for.

Detailed instructions on the use of the program are given in the README files. Further information about this suite of programs can be obtained from reference 1.

Top | Next | Prev


  • Miguel Angel Yescas-Gonzalez, Modelling the Microstructure and Mechanical Properties of Austempered Ductile Iron, Ph.D. Thesis, University of Cambridge, 2001.
  • D.J.C. MacKay, 1997, Mathematical Modelling of Weld Phenomena 3, eds. H. Cerjak & H.K.D.H. Bhadeshia, Inst. of Materials, London, pp 359.
  • D.J.C MacKay's website at http://wol.ra.phy.cam.ac.uk/mackay/README.html#Source_code
  • Top | Next | Prev


    Input parameters

    The input variables for the model are listed in the README or README.DOC  file in the corresponding directory. The maximum and minimum values for each variable are given in the file MINMAX.

    Output parameters

    These program gives the tensile elongation in '%' . The corresponding output files is called Model_RESULT.dat or Result. The format of the output file is:
    Prediction     Error bar      Lower-limit      Upper-limit    
        (%)                       (%)              (%)
    Top | Next | Prev

    Error Indicators


    Top | Next | Prev


    A full calculation of the error bars is presented in reference 2.

    Top | Next | Prev

    Further Comments


    Top | Next | Prev


    1. Program text

           Complete program.

    2. Program data

    See sample data file: test.dat.

    3. Program results

    See sample output file: Result or Model_RESULT.dat.

    Top | Next | Prev

    Auxiliary Routines


    Top | Next | Prev


    neural network, tensile elongation, ADI,  Austempered ductile cast iron, bainite

    Top | Next | Prev


    Download ADI_ELONGATION model (gzip tar file, 5.4 Mbytes)
    Download ADI_ELONGATION model (gzip tar file, 5.4 Mbytes)
    PC Software:
    Download ADI_ELONGATION model (5.7Mbytes)
    Top | Prev

    MAP originated from a joint project of the National Physical Laboratory and the University of Cambridge.
    MAP Website administration / map@msm.cam.ac.uk


    Top | Index | MAP HomepageValid HTML 3.2!