Effect of forging temperature on the structure, mechanical and functional properties of superelastic Ti-Zr-Nb bar stock for biomedical applications
Bibliographic record
Abstract
Ti-Zr-Nb shape memory alloys exhibit a unique combination of properties that make them suitable for bone implants: low Young’s modulus, superelastic behavior, superior corrosion resistance, and non-toxicity of all the constitutive elements. In this study, superelastic Ti-19Zr-14Nb (at.%) alloy was subjected to a combination of radial shear rolling at 900°C and rotary forging in a temperature range from 500 to 700°C to form long-length bar stocks for bone implants fabrication. Features of the grain structure, phase composition, mechanical and functional properties of the long-length bar stocks were analyzed using light microscopy, X-ray analysis, as well as during mechanical and functional tests. It was shown that after radial shear rolling at 900°C, a heterogeneous grain structure was formed over the cross-section of the bar stock, and this structure was inherited after the subsequent rotary forging at 500°C. With an increase in the forging temperature, the structural heterogeneity is eliminated and the grain size increases, while the hardness and strength characteristics of the material decrease. After rotary forging at 700°C, the alloy manifests the best combination of structural, mechanical, and functional characteristics. In this state, the long-length bar stock demonstrates a homogeneous grain structure with a certain fraction of a dynamically polygonized substructure of β-phase, a satisfactory strength (UTS ≈ 580 MPa), a low Young’s modulus (E ≈ 35 GPa), the high difference between dislocation and phase yield strength (Δσ ≈ 280 MPa), and a relatively large amount of superelastic recovery strain (εrSEmax ≈ 3.1%).
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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".