Hyperelastic modelling of rubber behaviour in finite element software
Bibliographic record
Abstract
Experimental characterisation of rubber in uniaxial, equi-biaxial and planar tension under cyclic quasi-static loading shows strain-induced stress softening, hysteresis and unrecoverable strain. The objective of this work is to study the applications and limitations involved in predicting the behaviour of rubber with hyperelastic models. To assume a preconditioned perfectly elastic material, the data obtained from experiments must first be simplified. The data is then fitted to popular hyperelastic models in the finite element analysis (FEA) software ANSYS(TM). A single hyperelastic model (with given coefficients) is shown to only provide a good fit to a single characterisation test and level of preconditioning at the time. A two-iteration preconditioning method is developed using different hyperelastic models for a given material to approximate the softening effect of cyclic loading in a static FEA simulation. A biaxiality test is developed, providing information on the dominant mode of simple strain in the elements of a FE model. FEA simulations and experimental tests of a cantilevered rubber plate subjected to a bending load at its free end as well as a rubber guide lug subjected to a transverse deflection are presented and discussed. It is shown that using a single hyperelastic model is insufficient to predict the behaviour of these experiments in FEA simulations. The preconditioning iteration, when applied to these simulations, shows very good agreement with the experiments, both qualitatively and quantitatively. The biaxiality test provides insight on which characterisation test is the most appropriate for curve fitting hyperelastic models for a given analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".