Materials Algorithms Project
of subroutine's operation.
Provenance of Source CodeCarlos Capdevila, Francisca G. Caballero and Carlos Garcia de Andres,
Phase TRansformation group (GITFES),
National Center for Metallurgical Research (CENIM),
The neural network program was produced by:
University of Cambridge,
Cambridge, CB3 0HE, U.K.
Added to MAP: August 2000
PurposeEstimation of the Ms temeprature in steels as a function of
chemical composition and previous austenite grain size (PAGS).
||FORTRAN / C|
||Source code / Executable files|
||Windows 95/98/2000 |
contains a suite of programs which enable the user to estimate the Ms temperature as a
function of chemical composition and previous austenite grain size. 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 .
16 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 .
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 PC under Windows 95/98/2000. A set of program and data files are
provided for the model, which calculate the Ms temperature in steels.
The files for PC are included in a directory called MS_files. 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
- An input text file containing the input variables used for predictions.
- This executable program for the PC.
- This is a text file which contains the normalised input variables. It is
generated by the program MS.exe.
- This is the executable file for the neural network program.
- It reads the normalised input data file and uses the weight files in subdirectory
c. The results are written to the temporary output file
- Contains the final un-normalised committee results for the predicted
percentage retained austenite.
- SUBDIRECTORY s
- A text file containing the number of models to be used to form the
committee result and the number of input variables.
- A text file containing information about number of input variables and weight file for committee models.
- SUBDIRECTORY c
- 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.
- SUBDIRECTORY d
- A normalised output file which was created during the building of the
model. It is accessed by generate44.
- SUBDIRECTORY outprdt
- 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
- C. Capdevila, F.G. Caballero and C. Garcia de Andres, Prediction of Ms Temperature in Steels Submitted to
Materials Science and Technology, 2002.
- 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
Input parametersThe input variables for the model are listed in the
README.DOC file in the corresponding directory.
The maximum and minimum values for each variable are given in the file
Output parametersThese program gives the Ms temperature in 'K'. The corresponding output files is called Result.dat or
Result. The format of the output file is:
Prediction Error-Bar Upper-limit Lower-limit
(K) (K) (K) (K)Top
AccuracyA full calculation of the error bars is
presented in reference 1.
1. Program text
2. Program dataSee sample data file: test.dat.
3. Program resultsSee sample output file: Result or
Keywordsneural network, Ms temperature, Steels
Ms temperature Model
MAP originated from a joint project of the National Physical
Laboratory and the University of Cambridge.
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