Experimental validation of shape memory material model implemented in commercial finite element software under multiaxial loading
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it