An Approach to Improve the Resolution of DLP 3D Printing by Parallel Mechanism
Bibliographic record
Abstract
For 3D printing based on Digital Light Processing (DLP) technology, the tradeoff of size and resolution remains challenging due to the limitations of the existing techniques. Therefore, we propose an approach to improve the feature resolution without sacrificing the part size. It is achieved by changing the projection distance and then adjusting the projection resolution and format, which is different from the previous printing principle of fixed resolution. To achieve this process, the tripteron 3-Degree-of-Freedom (3-DoF) parallel mechanism is innovatively applied to the DLP 3D printing structure, which simplifies the control process. Since the projection is fixed on the motion platform, the projection distance changes as the platform moves in space. Then the PLC-based program is developed according to the motion process, which successfully runs on the established prototype. Finally, the experiments are designed through the orthogonal method to optimize the molding process parameters. The effectiveness of the approach is verified by the feature forming comparison experiment. The results show that it can reduce the size of features by about 1.3 times through adjusting the projection resolution under the same size parts. Our method provides a new way for solving the contradiction, but more research needs to be done.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".