Geometric Analysis of Thinning during Superplastic Forming
Bibliographic record
Abstract
Two original geometric models applicable to the superplastic forming of prismatic die shapes are presented in this study: the uniform thickness model which determines the average of the final thickness, and the variable thickness model, which gives a first approximation of the thickness distribution by assuming sticking contact with the die. The variable thickness approach demonstrates the important contribution of the die geometry to the thinning process by monitoring the sequence of contact events throughout the process. These two models relate to the limits of the friction regime between the superplastic material and the die, i.e. perfect sliding for the uniform thickness model (lubrication and/or low pressures/low strain rates) and sticking contact for the variable thickness model (no lubricant and/or high pressures/high strain rates). Predictions of the pressure-time profiles required to form the component are also derived from the second model and were successfully applied to the manufacture of dental prostheses in titanium alloy (Ti-6Al-4V). The development and the implementation of this geometric study have been greatly and elegantly simplified by the introduction of complex numbers to represent the different geometric parameters.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".