Functional Properties of Ti-Ni-Based Shape Memory Alloys
Bibliographic record
Abstract
The main functional properties (FP) of Ti-Ni Shape Memory Alloys (SMA) are their critical temperatures of martensitic transformations, their maximum completely recoverable strain (er,1 max) and maximum recovery stress (sr max). Control of the Ti-Ni-based SMA FP develops by forming well-developed dislocation substructures or ultrafine-grained structures using various modes of thermomechanical treatment (TMT), including severe plastic deformation (SPD). The present work shows that TMT, including SPD, under conditions of high pressure torsion (HPT), equal-channel angular pressing (ECAP) or severe cold rolling followed by post-deformation annealing (PDA), which creates nanocrystalline or submicrocrystalline structures, is more beneficial from SMA FP point of view than does traditional TMT creating well-developed dislocation substructure. ECAP and low-temperature TMT by cold rolling followed by PDA allows formation of submicrocrystalline or nanocrystalline structures with grain size from 20 to 300 nm in bulk, and long-size samples of Ti-50.0; 50.6; 50.7%Ni and Ti-47%Ni-3%Fe alloys. The best combination of FP: sr max =1400 MPa and er,1 max=8%, is reached in Ti-Ni SMA after LTMT with e=1.9 followed by annealing at 400°C which results in nanocrystalline (grain size of 50 to 80 nm) structure formation. Application of ultrafine-grained SMA results in decrease in metal consumption for various medical implants and devices based on shape memory and superelastiсity effects.
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.
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 | 0.000 |
| 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 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".