Effects of DLP printing orientation and postprocessing regimes on the properties of 3D printed denture bases
Bibliographic record
Abstract
STATEMENT OF PROBLEM: The variety of recommended postprocessing techniques and printing parameters makes it challenging to determine the best approach to 3-dimensionally (3D) printed dentures. PURPOSE: The purpose of this in vitro study was to assess the effect of printing orientations (0, 45, and 90 degrees) and postprocessing treatments (ultraviolet [UV], heat, or combination) on the mechanical and surface properties of 3D printed denture base resin. MATERIAL AND METHODS: Three-dimensionally printed denture base resin specimens were fabricated at 0-, 45-, and 90-degree printing orientations, followed by 4 postprocessing techniques (UV, Heat, UV+Heat, and control). Microhardness was assessed using a Vickers microhardness tester. Additionally, the flexural strength (FS) and modulus of elasticity (MoE) were analyzed using a 3-point bend test. Wettability was measured according to the sessile drop test. The fractured surfaces were observed under scanning electron microscopy (SEM). RESULTS: FS was significantly greater (P<.001) at a print orientation of 90 degrees (73.7 MPa) compared with 0 and 45 degrees (55.2 and 61.8 MPa). No significant difference in FS was found among postprocessing treatments (all complied with the International Organization for Standardization [ISO] requirements). The UV group had the highest MoE (up to 2061 MPa), followed by the heat-treated groups (up to 1412 MPa). The 45-degree print orientation showed the highest contact angle (CA) in almost all groups (CA=117.6±11.7), and UV led to higher hydrophilicity (CA=33.9±12.0). The effect of build orientation on the microhardness depended on the postprocessing technique with the highest value (23.4 ±1.3) achieved by UV postprocessing in combination with the 90-degree orientation. CONCLUSIONS: The optimal FS of 3D printed denture base resin is achieved when it is printed in a vertical orientation (90 degrees relative to the platform base). Thermal annealing as a postprocessing technique combined with UV can effectively enhance FS, induce favorable wettability, and reduce stiffness.
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 | 0.000 | 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".