Materials Algorithms Project
Program Library
MAP_AL_TEXTURE
- Provenance of code.
- Purpose of code.
- Specification.
- Description of program's operation.
- References.
- Parameter descriptions.
- Error indicators.
- Accuracy estimate.
- Any additional information.
- Example of code
- Auxiliary routines required.
- Keywords.
- Download source code.
- Links.
A. C. Papageorgiou,
MPhil in Materials Modelling,
Department of Materials Science and Metallurgy,
University of Cambridge,
Cambridge CB2 3QZ, U.K.
acp39@hermes.cam.ac.uk
Added to MAP: May 2001.
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A program for the estimation of volume fraction of S, brass, copper, cube and goss texture of an aluminium sample after recrystallization anneal as a function of true plane strains in four deformations, metal temperatures and strain rates of four deformations.
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Language: | FORTRAN / C
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Product form: | Executable files
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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
Al_texture_unix.tar.gz or Al_texture_linux.tar.gz
;The .tar.gz file contains the following files:
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README
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A manual containing step-by-step instructions for running the program,
including a list of input variables.
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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, together with an example set of data.
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test_result_S
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Contains the results you should expect from the example set of data, for the S texture. To test that the model is running properly on your computer, use
the given 'test.dat' file to do predictions and compare the 'result_S' file
with this file.
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test_result_copper
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Contains the results you should expect from the example set of data, for the copper
texture. To test that the model is running properly on your computer, use
the given 'test.dat' file to do predictions and compare the 'result_copper' file
with this file.
IDEM for test_result_brass, test_result_goss and test_result_cube
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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 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.
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spec.t1
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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.
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.generate_spec (hidden)
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This executable file creates a file called spec.t1, required by
generate44.
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.randomise (hidden)
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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
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.generate44 (hidden)
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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.
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.gencom (hidden)
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This executable file combines the predictions of the different models in the committee
and calculates the combined error bar.
.treatout (hidden)
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This executable un-normalise the committee predictions and produces the file 'result'.
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result_S, result_goss etc..
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Contains the final un-normalised committee results for the predicted output.
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SUBDIRECTORY copper
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- Contains all the data related to the model for copper texture, the substructure
of each of these folder (copper, cube, etc..) is as follows:
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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 for each model.
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_R*
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Files containing values for the noise, test error and log predictive error
for each model.
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_R*
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Files containing values for the noise, test error and log predictive error
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 during the building of the model.
It is accessed by generate44 via spec.t1.
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SUBDIRECTORY outprdt
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com.dat
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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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- 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
- True plane strain in first deformaion
- True plane strain in second deformation
- True plane strain in third deformation
- True plane strain in fourth deformation
- Metal temperature of first deformation (Centigrade)
- Metal temperature of second deformation (Centigrade)
- Metal temperature of third deformation (Centigrade)
- Metal temperature of fourth deformation (Centigrade)
- Strain rate of first deformation (per second)
- Strain rate of second deformation (per second)
- Strain rate of third deformation (per second)
- Strain rate of fourth deformation (per second)
Output parameters (example with result_S)
- predicted volume fraction of brass texture in sample after recrystallization anneal (%)
- error bar on volume fraction of S texture (%)
- volume fraction of S texture (%) - error bar
- volume fraction of S texture (%) + error bar
A more detailed description is presented in the README file.
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None.
Y ERROR INDICATORS -->
None.
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An estimated predictive error bar is provided by the model.
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???
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1. Download the model
Uncompress the "Al_texture_unix.tar.gz" (or "Al_texture_linux.tar.gz") file in a
dedicated directory (for example: "neural").
On UNIX systems, this is done by:
- gzip -d Al_texture_unix.tar.gz
- tar -xvf Al_texture_unix.tar
2. Program data
A default test.dat file is provided, containing four lines of data plus labels for
each variables. You may first use it and compare the ouput to the content of
test_result.txt
To do predictions on your own data, edit the testxt
To do predictions on your own data, edit the test.dat file.
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 "Results" files (result_S etc..),
as described in the README file.
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neural networks, aluminium, texture, strain, temperature, strain rate
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Download package (Solaris 5.5.1) (1 Mb)
Download package (Linux) (1 Mb)
Download package (IRIX) (1 Mb)
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