Experimental validation of shape memory material model implemented in commercial finite element software under multiaxial loading
Bibliographic record
Abstract
Shape memory alloys are used in ever-increasing numbers of applications, such as implants made of porous shape memory alloys, where the material is subjected to complex loading conditions with various loading paths. Finite element simulation of such parts requires utilizing a constitutive model that is able to capture the multiaxial and path-dependent behavior of shape memory alloys. The main objective of this article is to investigate the accuracy of the constitutive model implemented in current commercial finite element software such as Ansys in predicting the shape memory alloys mechanical response under different multiaxial loading paths. To this end, several isothermal tests were conducted on thin-walled NiTi tubes with uniaxial, as well as multiaxial, path-varying loadings. The performance of the material model within Ansys was then investigated by finite element modeling of the sample tubes and performing simulations of the tests. Comparing the finite element results with experimental data, it was observed that while this model provided a close prediction of the uniaxial tensile superelastic response, it was not able to reproduce the multiaxial and path-dependent behavior of the shape memory alloy samples with sufficient accuracy. A brief discussion of the reasons behind the inaccuracy of the current model and potentially promising models for future investigation are provided.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".