Thermomechanical Treatments and Their Influence on the Microstructure and Stress/Strain Diagrams of NiTi Shape Memory Alloys
Bibliographic record
Abstract
Structural changes in NiTi Shape Memory Alloys (SMA) subjected to Low Temperature Thermomechanical Treatments (LTMT) with subsequent Post-Deformation Annealing (PDA) are studied by X-ray and transmission electron microscopy (TEM) analyses. The influence of these structural changes on the mechanical behaviour of the two NiTi alloys, Ti-50.7 at.%Ni and Ti-50.0 at.%Ni, is then determined by room temperature mechanical testing. It is observed that the material softening accompanying the annealing heat treatment progresses more rapidly in the case of LTMT with the deformation of martensite than with the deformation of austenite. Moreover, the parameters of stress/strain diagrams plotted for these two alloys are a function of the temperature of their post-deformation annealing as well as of the initial structural and phase states of the material at the mechanical testing temperature.On étudie les changements structuraux d’alliages à mémoire de forme (SMA) au NiTi, soumis à des traitements thermomécaniques à basse température (LTMT) suivis par un recuit après déformation (PDA), au moyen d’analyses des rayons x et de microscopie électronique à transmission. L’influence de ces changements structuraux sur le comportement mécanique de deux alliages au NiTi, Ti-50.7% at. Ni et Ti-50.0% at. Ni, est ensuite déterminée par essais mécaniques à la température de la pièce. On observe que l’adoucissement du matériau accompagnant le traitement thermique de recuit progresse plus rapidement dans le cas du LTMT avec déformation de martensite que dans le cas de la déformation d’austénite. De plus, les paramètres des diagrammes contrainte-déformation de ces deux alliages, représentés graphiquement, varient en fonction de leur température de recuit après déformation ainsi qu’en fonction des états initiaux de structure et de phase du matériau à la température des essais mécaniques.
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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 | 0.000 |
| 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".