Functional properties of nanocrystalline, submicrocrystalline and polygonized Ti-Ni alloys processed by cold rolling and postdeformation annealing
Bibliographic record
Abstract
Thermomechanical processing consisting of cold rolling (e=0.3-2.0) and post-deformation annealing (300-450oC, 1h) was applied to binary Ti-Ni alloys to produce nanocrystalline structures (NS) or polygonized dislocation substructures (PDS), or their mixture. The evolution of the material structure and properties was studied using TEM, X-ray, microhardness, calorimetry and tensile testing techniques. Recovery stress and strain of the 50.26at%Ni alloy and superelastic strain of the Ti-50.6at%Ni alloy were measured under static and fatigue conditions. It was found that higher true yield stress of NS alloys not only increases the recovery stress potential, but, since it is combined with a relatively low transformation yield stress; it increases the completely recoverable strain. NS alloys generate recovery stresses that are twice as high as those of PDS alloys (1200 MPa), completely recoverable strains that are 10% greater (up to 6% in tension), and they demonstrate a higher cyclic stability of functional properties. This improvement comes with the cost of a lower NS alloy fatigue damage tolerance, aggravated by the presence of microcracks caused by cold working. Binary Ti-Ni alloys, processed by annealing of an intermediately cold-worked (e=0.75…1) alloy and containing mixed nanocrystalline structure and polygonized dislocation substructure, allow a high fatigue life combined with relatively high and cyclically stable functional properties.\n\n\n\nNote to the reader:\n\n On pages 01001-p4, 01001-p6 and 01001-p9 several mistakes have been corrected on October 19, 2009. \n\n\n\n\n\n
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 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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".