Materials Algorithms Project
Program Library

MAP_AL_TEXTURE

1. Provenance of code.
2. Purpose of code.
3. Specification.
4. Description of program's operation.
5. References.
6. Parameter descriptions.
7. Error indicators.
8. Accuracy estimate.
9. Any additional information.
10. Example of code
11. Auxiliary routines required.
12. Keywords.
13. Download source code.
14. Links.

Provenance of Source Code

A. C. Papageorgiou,
MPhil in Materials Modelling,
Department of Materials Science and Metallurgy,
University of Cambridge,
Cambridge CB2 3QZ, U.K.

Added to MAP: May 2001.

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Purpose

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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Specification

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Description

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:

README

A manual containing step-by-step instructions for running the program, including a list of input variables.

MINMAX

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.

test.dat

An input text file containing the input variables used for predictions, together with an example set of data.

test_result_S

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.

test_result_copper

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

model.gen

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.

spec.t1

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.

.generate_spec (hidden)

This executable file creates a file called spec.t1, required by generate44.

.randomise (hidden)

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

.generate44 (hidden)

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.

.gencom (hidden)

This executable file combines the predictions of the different models in the committee and calculates the combined error bar.

.treatout (hidden)

This executable un-normalise the committee predictions and produces the file 'result'.

result_S, result_goss etc..

Contains the final un-normalised committee results for the predicted output.

SUBDIRECTORY copper

Contains all the data related to the model for copper texture, the substructure of each of these folder (copper, cube, etc..) is as follows:

SUBDIRECTORY c

_w_*f

The weights files for the different models.

_*.lu

Files containing information for calculating the size of the error bars for the different models.

_c_*

Files containing information about the perceived significance value for each model.

_R_*

Files containing values for the noise, test error and log predictive error for each model.

_R_*

Files containing values for the noise, test error and log predictive error for each model.

SUBDIRECTORY d

outran.x

A normalised output file which was created during the building of the model. It is accessed by generate44 via spec.t1.

SUBDIRECTORY outprdt

com.dat

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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References

1. 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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Parameters

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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Error Indicators

None.

Y ERROR INDICATORS --> None.

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Accuracy

An estimated predictive error bar is provided by the model.

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Further Comments

???

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Example

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

3. Running the program (making predictions)

4. Results of the program (predictions)

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Auxiliary Routines

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Keywords

neural networks, aluminium, texture, strain, temperature, strain rate

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Download

Download package (Solaris 5.5.1) (1 Mb)
 
Download package (Linux) (1 Mb)
 
Download package (IRIX) (1 Mb)

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