Three-roll four-stand hot longitudinal rolling of superelastic Ti-Zr-Nb alloy bars for orthopedic implants: finite element modeling and experimental study
Bibliographic record
Abstract
Abstract Finite element modeling and experimental study of the three-roll four-stand hot longitudinal rolling (LR) deformation process were conducted for a Ti–Zr–Nb shape memory alloy. The stress, strain, strain rate, temperature, and triaxiality fields were analyzed in detail. During the process, bar stock predominantly experienced compressive stresses of up to 780 MPa, while the tensile stresses remained below 100 MPa. Triangular calibers generated higher compressive stresses than their circular counterparts, and the stress triaxiality remained below a critical threshold for the defect formation, thus ensuring process stability. The equivalent strain and strain rate distributions were strongly non-uniform, exhibiting six surface-localized maxima that led to the related microstructure and hardness gradients. The strain rate exceeded 50 s −1 on the surface and decreased to 15–25 s −1 in the core of the bar stock, while the deformation-induced heating raised the billet temperature from 700 °C to approximately 850 °C. Microstructural observations confirmed the grain refinement and anisotropy reduction after the final deformation stand. The processed alloy demonstrated a favorable combination of the mechanical and functional properties ( UTS = 728 MPa, ϵ r se max = 3.6%, E = 57 GPa), thus proving that the three-roll LR process is an effective method for producing superelastic Ti-alloy bar stock for biomedical applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".