Role of the structure and texture in the realization of the recovery strain resource of the nanostructured Ti-50.26 at %Ni alloy
Bibliographic record
Abstract
In this work, we have studied the nanostructure, the crystallographic texture, and the crystal lattice of the martensite of the Ti-50.26 at % Ni alloy subjected to a thermomechanical treatment, which includes cold rolling, warm (at 150°C) rolling, intermediate and post-deformation annealings (at 400°C) in different combinations. To calculate the resource of the recovery strain in the approximation of a polycrystal, we suggested and employed a method based on the sufficiently complete allowance for the orientation distribution function of the initial B 2 austenite and on the assumption on the realization of the most favorable orientational variant of martensite in each grain. The calculated values of the resource of the recovery strain have been compared with the experimental data and have been analyzed along with the results of the determination of the recovery stresses and parameters of the loading-unloading diagram. Estimations have been made of the role of the structural and textural factors in the realization of the recovery strain of the nanostructured Ti-50.26 at % Ni alloy. To achieve the maximally high recovery strain, one should focus on obtaining a nanocrystalline structure in combination with a sharp texture, which ensures the maximum transformation deformation in the direction of tension.
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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".