Effect of support arrangements on 3D printing denture accuracy: An <i>in vitro</i> study
Bibliographic record
Abstract
PURPOSE: Supports are essential for ensuring dimensional accuracy in 3D printing; however, an excessive number of supports compromises printing efficiency. This study aimed to investigate how a varying number of support arrangements affects the precision and trueness of 3D-printed dentures. METHODS: Three denture base printing files were designed, each with different numbers of supports: 40 (group 40), 55 (group 55), and 70 (group 70). Thirty samples were printed and measured across the groups. Accuracy was evaluated by assessing trueness and precision using the root mean square error (RMSE). The error areas in each group were analyzed through micro-computed tomography (micro-CT) 3D imaging. RESULTS: Group 70 showed a significantly lower RMSE for trueness than Group 40 (P < 0.05), but showed no significant difference from Group 55 (P ≥ 0.05). For precision, Group 70 outperformed both Groups 40 and 55 (P < 0.05), which did not differ significantly (P ≥ 0.05). Micro-CT revealed no mismatches in the palatal region. Discrepancies-areas where the supports in Groups 40 and 55 did not accurately align with those in Group 70-were predominantly observed at initiation points of overhangs in thinner sections. CONCLUSIONS: Based on these results, this study recommends placing support structures strategically around overhangs and thin-walled areas to enhance the accuracy of 3D-printed denture fabrication. These findings indicate that optimizing support placement, rather than merely increasing the number of supports, is crucial in improving the quality and reliability of 3D-printed dental prostheses.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".