A TMPS-designed personalized mandibular scaffolds with optimized SLA parameters and mechanical properties
Bibliographic record
Abstract
With the rapid development of 3D printing technology, porous titanium scaffolds have provided a new restoration method to repair bone defects. Compared with the traditional body-centered cubic (bcc) dot matrix structure with a simple arrangement and repetitive structure, the topology-driven properties of triply periodic minimal surfaces (TPMS) can offer a continuous surface and smooth curvature, an excellent platform for cell proliferation. In this study, we used reverse engineering techniques to model the mandible. Sheet and solid networks of gyroid structure, the most common type of TPMS, were selected for porous design and then molded using metal 3D printing technology. At the same time, the surface treatment parameters of sandblasted, large-grit, and acid-etched (SLA) were optimized by orthogonal experimental design. Then, the optimized SLA parameter was used to treat the gyroid with 70% porosity. The result showed that reverse engineering reconstructed the TPMS-based mandibular model had good formability. Furthermore, the best surface morphology, wettability, and roughness were obtained for 3D printed Ti6Al4V under the treatment of 80 mesh Al 2 O 3 , blasting distances of 4 cm, and a 1:1:2 acid ratio. Moreover, the mechanical properties of Sheet-Gyroid and Solid-Gyroid were significantly different at 70% porosity. The porosity of the scaffolds was close to the design porosity after SLA treatment. However, no significant changes were found in its mechanical properties, all matching the mandible’s mechanical properties to meet the implantation conditions.
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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 | 0.000 |
| 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 | 0.000 | 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".