MAP Logo

Materials Algorithms Project
Steels Program Library

[A logo showing the University of Cambridge Crest]


  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

R. Kemp
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 2004

Top | Next


Estimation of yield stress of irradiated reduced activation ferritic/martensitic (RAFM) steels under tensile testing as a function of irradiation conditions (overall damage (dpa), He production, irradiation temperature) and tensile test temperature.

Top | Next | Prev


Language: FORTRAN / C
Product form: Source code / Executable files
Operating System: Solaris 5.5.1; Macintosh OSX; Linux

Top | Next | Prev


MAP_NEURAL_SS_IRR_RAFM  contains a suite of programs which enable the user to estimate the yield stress of a tensile specimen of irradiated RAFM steel as a function of irradiation and test 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 database of experimental results [4, and references therein]. 5 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 has been found that a more accurate result could be obtained by averaging the results from all the models [1]. 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 Solaris 5.5.1, Linux, and Macintosh OSX. A set of program and data files are provided for the model, which calculate the yield stress (MPa). The files are included in a directory called SS_IRR_RAFM. 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 shell script containing the command steps required to run the module. It can be executed by typing ./model.gen  at the command prompt in a terminal. 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.
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.
This is a text file which contains the normalised input variables. It is generated by the program normtest.for in subdirectory s.
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 _out.
_ot, _out, _res, _sen
These files are created by generate44 and should be deleted automatically.
Contains the final un-normalised committee results for the predicted elongation, along with a calculation of the modelling uncertainty.
The source code for program spec.ex.
Program to normalise the data in test.dat and produce the normalised input file 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.
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.
Program to calculate dependent inputs (total He concentration, for example). Reads from run_data.dat and writes to test.dat.
Program to calculate actual % elongation from the model outputs. These outputs are then written to final_result
Program to count the number of input data.
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 file. Further information about this suite of programs can be obtained from reference 3.

Top | Next | Prev


  1. H. K. D. H. Bhadeshia, Neural Networks in Materials Science, ISIJ International 39 (1999) No. 10, 966 - 979.
  2. D.J.C. MacKay, Mathematical Modelling of Weld Phenomena 3 (1997), eds. H. Cerjak & H.K.D.H. Bhadeshia, Inst. of Materials, London, pp 359.
  3. D.J.C MacKay's website at
  4. R Kemp, et al., Neural Network Analysis of Irradiation Hardening in Low-Activation Steels, Journal of Nuclear Materials 348 (2006) 311-328
Top | Next | Prev


Input parameters

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

Output parameters

These program gives the yield stress in MPa. The corresponding output files is called final_result. The format of the output file is:
Prediction     Negative uncertainty      Positive uncertainty
   (MPa)                  (MPa)                     (MPa)
Top | Next | Prev

Error Indicators


Top | Next | Prev


This model was trained on a limited dataset, and close attention should be paid to the modelling uncertainties. See 4 for more details.

Top | Next | Prev

Further Comments

A report on the creation of this model is available [4].

Top | Next | Prev


1. Program text

       Complete program.

2. Program data

See sample data file: run_data.dat.

3. Program results

See sample output file: final_result.

Top | Next | Prev

Auxiliary Routines


Top | Next | Prev


neural networks, yield stress, ductility, irradiation, ferritic steel, martensitic steel, RAFM

Top | Next | Prev


Download RAFM_YS_IRR model (gzip tar file, 788 Kb)
Download RAFM_YS_IRR model (gzip tar file, 755 Kb)
Macintosh OSX:
Download RAFM_YS_IRR model (gzip tar file, 783 Kb)
Top | Prev

Superalloys Titanium Bainite Martensite Widmanstätten ferrite
Cast iron Welding Allotriomorphic ferrite Movies Slides
Neural Networks Creep Stainless Steels Theses

PT Group Home Any Valid CSS!