Dental Implant Superstructures by Superplastic Forming
Bibliographic record
Abstract
A novel application of superplastic forming is described for the production of fixed-bridge dental implant superstructures. Finite element analysis (FEA) has shown that Ti-6Al-4V sheet would be a suitable candidate material for the design of a fixed-bridge dental implant superstucture. Traditionally superstructures are cast in gold alloy onto pre-machined gold alloy cylinders but castings are often quite bulky and 25% of castings do not fit accurately (1) which means that sectioning and soldering is required to obtain a fit that is clinically acceptable and will not prejudice the integrity of the commercially pure cp-titanium implants osseointegrated with the bone. Superplastic forming is shown to be a forming technique that would allow the production of strong, light-weight components of thin section with low residual stress that could be suitable for such applications. Considerable cost savings over traditional dental techniques can be achieved using a low-cost ceramic die material. The properties of these die materials are optimised so that suitable components can be produced. Satisfactory hot strength is demonstrated and thermal properties are matched to those of the titanium alloy for accurate fit of the prosthesis.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".