An experimental investigation on the interaction effect of cyclic loading parameters on the mechanical behavior of superelastic NiTi
Bibliographic record
Abstract
Shape memory alloys (SMAs) are ideal for passive vibration control systems due to their energy dissipation capability under cyclic loading and exceptional superelasticity. Existing studies show that the mechanical properties of SMAs can vary significantly depending on the cyclic loading conditions. There is a complex interaction between the combined effect of cyclic loading parameters and internal variables that primarily governs the mechanical behavior of SMAs. While the effects of individual cyclic loading parameters on the mechanical behavior of SMAs have been widely studied, the interaction of these factors remains largely underexplored in the existing literature. The current study systematically explores the interaction of loading frequency, pre-strain, and strain amplitude on the mechanical response of superelastic NiTi, particularly in terms of energy dissipation, residual strain, and effective stiffness through an experimental approach that considers two of the parameters simultaneously. Moreover, theoretical rationalization for the observed SMA wire mechanical behavior resulted from complex interactions of different loading parameters have been offered, a dimension largely omitted in existing studies. This not only explains the inconsistent observations reported in literature, but also provides a predictive perception for SMA behavior beyond the tested parameter ranges.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".