Pseudo-Test Data for an Elastomer

This data has been quite useful to me and perhaps you might find it useful too.  From our elastomer class at Axel Products, I had some test data for a silicone rubber material.  But, for calibration purposes I wanted to have a wider suite of test types.   So I took the limited set of test data that I had and created a reasonable Yeoh+Prony linear viscoelastic material model for it.   Then I used a series of Abaqus runs (using a unit cube) to generate a fuller set of test data.   Now, I have a fuller set of test data for trying out calibration strategies, but I also now have the "right answer" since I know that all of this pseudo-test data originated from one single set of Hyper+Prony coefficients.  There are a total of

3 tests - fast, mid and slow rate tests, all uniaxial, monotonic pull-tests.

3 tests - uniaxial, biaxial and planar at a strain-rate of 1e-2

3 tests - uniaxial, biaxial and planar at a strain-rate of 1e-1

3 tests - stress relaxation to initial strains of 20%, 50% and 100%.

The material model that i used to generate all the pseudo-test data is:

*****************************************************

MATERIAL, NAME=Rubber

*Hyperelastic, Yeoh, Moduli=Instantaneous

0.600, -0.0600, 0.0108, 0.0

*Viscoelastic, Time=Prony

0.20,,     0.1   

0.18,,     1.0

0.15,,    10.0

0.10,,   100.0

0.05,,  1000.0

*****************************************************

Abbreviations:

ST == Simple Tension (uniaxial tension)

EB == Equi-Biaxial

PT == Planar Tension

This file (below) is the smaller of two Excel files for download, This file size is 913KB.

This file (below) is the larger of two Excel files for download.  This file size is 59,016 KB.

This file contains some very large datasets, the largest is the ST_cyclic dataset containing ~1e6 datapoints.

Generally you should avoid using large datasets, this was just done for testing purposes.  Large datasets will seriously degrade the calibration app's performance.

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