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Subroutine MAP_STEEL_FLANGEABILITY

  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

Mr S Chatterjee, December 2006, sc446@cam.ac.uk
Phase Transformations and Complex Properties Research Group,
Materials Science and Metallurgy,
University of Cambridge,
U.K.

The neural network program was produced by:

David MacKay,
Cavendish Laboratory,
University of Cambridge,
Madingley Road,
Cambridge, CB3 0HE, U.K.

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Purpose

There are a number of programs included.

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Specification

Language: executables, C
Product form: executables

Description

Stretch-flangeability measures the ability of a material to be formed into a complex shape. A hole is initally punched on a steel blank. The hole is then allowed to expand under load until cracks develop at the surface. The ratio of the increase in diameter to the original diameter of the hole, referred to as hole expansion ratio, is used to determine the flangeability. An attempt is made here to represent the flangeability in terms of tensile test data.

Data used to create the model were collected from references [1-5]. The actual research is described in [6].

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 (details may differ between LINUX and PC versions):

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.
model.gen or model.exe
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.
spec.t1
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.
norm_test.in
This is a text file which contains the normalised input variables. It is generated by the program normtest.for in subdirectory s.
generate44
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.
_ot, _out, _res, _sen
These files are created by generate44 and can be deleted.
Result
Contains the final un-normalised committee results for the predicted hardness.
SUBDIRECTORY s
spec.c
The source code for program spec.ex.
normtest.for
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.
gencom.for
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.
treatout.for
Program to un-normalise the committee results in com.dat and write the output predictions to unnorm_com. This file is then renamed Result.
committee.dat
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.
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 [1] for each model.
_R*
Files containing values for the noise, test error and log predictive error [1] for each model.
SUBDIRECTORY d
outran.x
A normalised output file which was created when developing the model. It is accessed by generate44 via spec.t1.
SUBDIRECTORY outprdt
out1, out2 etc.
The normalised output files for each model.
com.dat
The normalised output file containing the committee results. It is generated by gencom.for.

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References

  1. Sudo, M., Tsukatani, I. and Shibata, Z., Effect of microstructure on the plastic anisotropy and mechanical properties of triphase sheet steel, Metallurgy of Continuous Annealed Sheet Steel, AIME (1982), 310-319
  2. Sugimoto, K-I., Nagasaka, A., Kobayashi, M. and Hashimoto, S. I., Effects of retained austenite parameters on warm stretch-flangeability in TRIP-aided dual phase sheet steels, ISIJ International 39 (1999) 56-63
  3. Hasegawa, K., Kawamura, K., Urabe, T. and Hosoya, Y., Effects of microstructure on stretch-flange-formability of 980 MPa grade cold-rolled ultra high strength steel sheets, ISIJ International 44 (2004) 603-609
  4. Fang, X., Fan, Z., Ralph, B., Evans, P. and Underhill, R., Effects of tempering temperature on tensile and hole expansion properties of a C-Mn steel, Journal of Materials Processing Technology 132 (2003) 215-218
  5. Fang, X., Fan, Z., Ralph, B., Evans, P. and Underhill, R., The relationships between tensile properties and hole expansion property of C-Mn steels, Journal of Materials Science 38 (2003) 3877-3882
  6. S. Chatterjee and H. K. D. H. Bhadeshia, Materials Science and Technology, in press, 2007.

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Parameters

2. Program number 1, inputs

Inputs in sequence YS (MPa), UTS (MPa), UEL (%), YR, UTS-UEL (MPa-%). The following example has three sets of inputs.

    
472    736    27    0.64    19872 
418    720    27    0.58    19440 
518    834    32    0.62    26688 


3. Program number 1, results

Outputs in sequence hole expansion ratio (%), followed by a one sigma modelling uncertainty. The outputs listed below correspond to the inputs described above.

  

37.16    25.19
43.72    25.27
29.39    26.14


2. Program number 2, inputs

Inputs in sequence UTS (MPa), UEL (%). The following example has three sets of inputs.

    
736    27 
720    27 
834    32 

3. Program number 2, results

Outputs in sequence hole expansion ratio (%), followed by a one sigma modelling uncertainty. The outputs listed below correspond to the inputs described above.

  
37.80    33.73
40.72    33.83
29.50    33.99

2. Program number 3, inputs

Inputs in sequence YS (MPa), UEL (%). The following example has three sets of inputs.

    
472    27 
418    27 
518    32 

3. Program number 3, results

Outputs in sequence hole expansion ratio (%), followed by a one sigma modelling uncertainty. The outputs listed below correspond to the inputs described above.

  
41.41    37.76
60.45    39.60
37.63    37.93

2. Program number 4, inputs

Inputs in sequence YS (MPa), UTS (MPa), TEL (%), YR, UTS-TEL (MPa-%). The following example has three sets of inputs.

    
472    736    32    0.64    23552 
418    720    31    0.58    22320 
518    834    36    0.62    30024 

3. Program number 4, results

Outputs in sequence hole expansion ratio (%), followed by a one sigma modelling uncertainty. The outputs listed below correspond to the inputs described above.

  
31.68    10.79
32.42    10.69
33.27    11.06

2. Program number 5, inputs

Inputs in sequence UTS (MPa), TEL (%). The following example has three sets of inputs.

    
500    10 
650    20 
800    30 

3. Program number 5, results

Outputs in sequence hole expansion ratio (%), followed by a one sigma modelling uncertainty. The outputs listed below correspond to the inputs described above.

  
160.27   19.44
77.44    13.28
30.55    11.98

2. Program number 6, inputs

Inputs in sequence UTS (MPa), UTS-TEL (MPa-%). The following example has three sets of inputs.

    
583    21979.1 
561    13520.1 
536    13989.6 

3. Program number 6, results

Outputs in sequence hole expansion ratio (%), followed by a one sigma modelling uncertainty. The outputs listed below correspond to the inputs described above.

  
50.42     15.21
128.45    14.13
136.09    13.73

2. Program number 7, inputs

Input to the model is UTS-TEL (MPa-%). The following example has two sets of inputs.

    
30024 
32379 

3. Program number 7, results

Outputs in sequence hole expansion ratio (%), followed by a one sigma modelling uncertainty. The outputs listed below correspond to the inputs described above.

  
35.52    30.56
36.85    31.42

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Auxiliary Routines

None.

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Keywords

neural network, stretch-flangeability, tensile test

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Download

Download source code, program 1 (Linux)

Download source code, program 2 (Linux)

Download source code, program 3 (Linux)

Download source code, program 4 (Linux)

Download source code, program 5 (Linux)

Download source code, program 6 (Linux)

Download source code, program 7 (Linux)

Download source code, program 1 (PC)

Download source code, program 2 (PC)

Download source code, program 3 (PC)

Download source code, program 4 (PC)

Download source code, program 5 (PC)

Download source code, program 6 (PC)

Download source code, program 7 (PC)

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MAP originated from a joint project of the National Physical Laboratory and the University of Cambridge.

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