Surface Properties of Polymer Resins Fabricated with Subtractive and Additive Manufacturing Techniques
Bibliographic record
Abstract
This study aimed to compare the surface roughness, hardness, and flexure strength of interim indirect resin restorations fabricated with CAD-CAM (CC), 3D printing (3D), and conventional techniques (CV). Twenty disk (3 mm × Ø10 mm) and ten bar specimens (25 × 2 × 2 mm) were fabricated for the CC, 3D, and CV groups, to be used for surface roughness, micro-hardness, and flexural strength testing using standardized protocol. Three indentations for Vickers micro-hardness (VHN) were performed on each disk and an average was identified for each specimen. Surface micro-roughness (Ra) was calculated in micrometers (μm) using a 3D optical non-contact surface microscope. A three-point bending test with a universal testing machine was utilized for assessing flexural strength. The load was applied at a crosshead speed of 3 mm/min over a distance of 25 mm until fracture. Means and standard deviations were compared using ANOVA and post hoc Tukey–Kramer tests, and a p-value of ≤0.05 was considered statistically significant. Ra was significantly different among the study groups (p < 0.05). Surface roughness among the CC and CV groups was statistically comparable (p > 0.05). However, 3D showed significantly higher Ra compared to CC and CV samples (p < 0.05). Micro-hardness was significantly higher in 3D samples (p < 0.05) compared to CC and CV specimens. In addition, CC and CV showed comparable micro-hardness (p > 0.05). A significant difference in flexural strength was observed among the study groups (p < 0.05). CC and 3D showed comparable strength outcomes (p > 0.05), although CV specimens showed significantly lower (p < 0.05) strength compared to CC and 3D samples. The 3D-printed provisional restorative resins showed flexural strength and micro-hardness comparable to CAD-CAM fabricated specimens, and surface micro-roughness for printed specimens was considerably higher compared to CAD-CAM and conventional fabrication techniques.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".