Development of titanium dental implant by MIM : experiments and simulation
Bibliographic record
Abstract
Present work at IMI deals with the development of a composite porous/dense dental implant based on two powder metallurgy approaches: Metal injection molding (MIM) of titanium for the dense core and titanium foam coating for the outer surface. A first series of MIM dense cores were produced using a wax based binder and CpTi -45μm powder and some defects and instabilities were observed in both the molded and sintered components. This paper will focus on the understanding of the origin of these defects. To achieve this goal, several MIM feedstocks were prepared with the same volume of wax based binder but with powders having different particle sizes. A commercial feedstock was also used as a reference. The physical and rheological properties of these feedstocks were first characterized in order to obtain the data required for the full 3D modelling of the mold filling. The filling behaviour of these feedstocks was then examined through the production of short shots and the characterization of the molded parts by micro CT tomography. These experimental results were further compared with the mold filling simulation of the feedstocks using a 3D finite element code including free-surface flow.
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) | 0.000 | 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 | 0.000 |
| 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".