Effect of training on the cyclic behaviour of SMA wire
Bibliographic record
Abstract
Abstract Shape memory alloys (SMAs) are a new generation of smart metallic materials with numerous unique and widely applicable characteristics. With their superelasticity and ability to dissipate energy under cyclic loading, SMAs are an excellent choice for passive vibration energy dissipation systems. However, due to functional fatigue, the energy dissipation and re-centring capacity of virgin SMA dwindles at a decreasing rate during cyclic loading and eventually reaches a stable level. Since for vibration control applications stable mechanical properties with predictable responses to vibrational forces are preferred, preloading SMA wires for mechanical training is proposed to overcome this drawback. Nevertheless, the effect of training conditions on the mechanical behaviour of SMA wires has only been investigated in a few studies. To fill this research gap, the influence of different training parameters, such as strain amplitude, frequency, number of cycles and prestrain, on the mechanical behaviour of SMA wires is examined. The results show that while a sufficient number of cycles and certain level of strain amplitude are required to reach a stable stress–strain relation, training frequency is the most important parameter for eliminating residual strain.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".