Predicting the formation characteristics of titanium alloy self-locking nuts
Bibliographic record
Abstract
Titanium alloy is important for the aerospace industry because of its high specific strength as well as excellent anti-corrosion and anti-oxidative properties. In this paper, a three-dimensional thermo-mechanical coupled simulation was carried out to predict the formation characteristics of a TC4 titanium alloy self-locking nut during the upset forging process. The stability of the upset-forged material was analyzed, and the influences of initial temperature and deformation velocity on the quality of the formed material were investigated. The results show that if the length:diameter ratio of the sample is less than 3.27, the upset-forged material tends to be stable, so we selected a length:diameter ratio of 2.89. Additionally, the properties of the TC4 self-locking nut improved when the initial temperature was increase, and decreased when the velocity of the upper die was increased. Our results provide a theoretical guidance for the formation of TC4 titanium alloy nuts using upset forging.
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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 | 0.001 |
| 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 | 0.001 | 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".