Prototype design of porous Ti6Al4V intraosseous implant for use in mandibular reconstruction
Bibliographic record
Abstract
The design of patient-specific implants often requires computer simulations for the characterization of mechanical properties before manufacturing. We previously developed numerical models to predict the mechanical properties of porous Ti6Al4V constructs built using laser powder bed fusion (LPBF). Here, we developed a patient-specific porous intraosseous mandibular implant based on the models and techniques described in our previous research. The implant model used a simple cubic porous design with an average unit cell size of 1 mm and strut thicknesses between 350 and 450 μm. Finite element analysis was used to simulate right molar clenching on the mandible with and without the implant, under static and dynamic loading. The simulation showed that the implant would remain intact during right molar clenching and should not cause stress shielding. The fatigue numerical models predicted that the implant would remain functional under cyclic masticatory forces (50-100 N) for a period ranging between 4 and 119 years. Given that, within one year, bone ingrowth and osseointegration are complete, the implant is predicted to remain intact long-term under physiological loading conditions. These findings demonstrate the potential of computational modelling in developing patient-specific designs for porous implants built through LPBF.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".