Comparative Study of Superelastic Ti–Zr–Nb and Commercial VT6 Alloy Billets by QForm Simulation
Bibliographic record
Abstract
Abstract A comparative simulation of hot radial shear rolling (RSR) of billets made of a superelastic Ti–Zr–Nb and a commercial VT6 alloy was performed using the QForm finite element modeling program. Rolling in 48 modes with a variable feed angle and elongation ratio at 4 levels and initial rolling temperature at 3 levels was investigated for each alloy. The Ti–Zr–Nb alloy rheology during hot deformation was determined experimentally by hot upset forging and imported into the QForm program. The presence of maxima on the flow curves at the initial stage of deformation, which are absent in the VT6 alloy, is revealed. Simulation results are presented in the form of fields of the stiffness coefficient, strain rate intensity, cumulative strain degree in the maximum reduction section depending on the rolling mode. General regularities of the Ti–Zr–Nb and VT6 behavior in RSR are similar. The gradient of the fields studied decreases, and the roll pressure and torque increase with an increase in the feed angle and elongation ratio. The initial rolling temperature does not significantly affect the deformation pattern, but it significantly affects the roll pressure and torque. At the same time, the experimental alloy demonstrated the greater tendency to localize deforming forces in the near-contact zone and to increase the gradient of stress-strain state parameters over the billet section. The study of the tightening shape and depth of rolled billet ends showed that the Ti–Zr–Nb alloy has a 3.5–9.6% greater tightening depth. It is shown that experimental alloy rolling requires 1.6–2.4 times higher roll pressure and torque as compared to the commercial alloy.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.003 | 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".