Materials Algorithms Project
Program Library
Program MAP_STEEL_LOWTEMPER
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Provenance of code.
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Purpose of code.
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Specification.
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Description of subroutine's operation.
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References.
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Parameter descriptions.
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Error indicators.
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Accuracy estimate.
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Any additional information.
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Example of code
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Auxiliary subroutines required.
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Keywords.
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Download source code.
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Links.
Provenance of Source Code
D. Gaude-Fugarolas,
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: 2005
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Purpose
Estimation of the evolution of hardness (Vickers) during tempering
of Fe-0.55C-0.22Si-0.77Mn-0.2Cr-0.15Ni-0.05Mo-0.001V wt% steel at the range of temperatures in which only diffusion of carbon
and precipitation of carbides occur, without any recovery or recrystallisation.
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Specification
Language: |
FORTRAN / C |
Product form: |
Source code / Executable files |
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Description
MAP_STEEL_LOWTEMPER contains a trained model to estimate the
change in hardness in quenched carbon steel during tempering at low temperature.
It is considered low temperature the range in which only diffusion of carbon
and precipitation of carbides occur, excluding any substantial recovery
or recrystallisation. The model is based on an Avrami reaction rate equation
implemented by training an artificial neural network on a small but very
accurate database of experimental results. The model obtained has one single
submodel in committee. The training of the model makes use of a neural
network program called generate44, which was developed
by David MacKay and is part of the bigback5 program. The
model is trained using an interface developed by Sourmail. 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 downloadable package contains the following files and subdirectories:
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MINMAX
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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.
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test.dat
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An input text file containing the input variables used for predictions.
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model.gen
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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.
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spec.t1
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A dynamic file, created by spec.ex, which contains information
about the module and the number of data items being supplied. It is read
by the program generate44.
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norm_test.in
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This is a text file which contains the normalised input variables. It is
generated by the program normtest.for in subdirectory s.
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generate44
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This is the executable file for the neural network program. 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.
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_ot, _out, _res, _sen
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These files are created by generate44 and can be deleted.
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Result
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Contains the final un-normalised committee results for the predicted hardness.
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SUBDIRECTORY s
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spec.c
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The source code for program spec.ex.
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normtest.for
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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.
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gencom.for
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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.
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treatout.for
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Program to un-normalise the committee results in com.dat
and write the output predictions to unnorm_com. This file
is then renamed Result.
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committee.dat
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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.
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SUBDIRECTORY c
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_w*f
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The weights files for the different models.
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*.lu
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Files containing information for calculating the size of the error bars
for the different models.
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_c*
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Files containing information about the perceived significance value [1]
for each model.
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_R*
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Files containing values for the noise, test error and log predictive error
[1] for each model.
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SUBDIRECTORY d
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outran.x
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A normalised output file which was created when developing the model. It
is accessed by generate44 via spec.t1.
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SUBDIRECTORY outprdt
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out1, out2 etc.
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The normalised output files for each model.
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com.dat
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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.
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References
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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.
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D.J.C MacKay's website at https://www.inference.phy.cam.ac.uk/mackay/
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Parameters
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
This program gives a normalised hardnes in 'HV'. The corresponding normalised
output files are called Model_RESULT.dat or Result.
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Error Indicators
None.
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Accuracy
A full calculation of the error bars is presented in reference 1.
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Further Comments
None.
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Example
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.
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Auxiliary Routines
None
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Keywords
neural network, hardness, tempering, low temperature
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Download
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Linux:
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Download Linux version
(zip archive)
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MAP originated from a joint project of the National Physical
Laboratory and the University of Cambridge.
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