Design and Flow Simulation of Resin Dispensing System for UV Laser-Based Extrusion 3D Printing Technique
Bibliographic record
Abstract
Stereolithography (SLA) is a 3D printing technology that uses a laser to cure liquid resin from a tank, layer by layer, to create highly detailed and precise objects. However, this 3D printing technique requires a large amount of resin material to be placed in the bath that is exposed to UV each time a layer is printed. On the other hand, extrusion 3D printing offers the printing of a part by dispensing only the desired amount of material on demand. However, extrusion 3D printing does not result in the intricate high-resolution features that can be achieved in SLA. To be able to 3D-print high-resolution parts without needing a large resin bath, a dispensing system was designed that would deposit only the required amount of resin on demand while high-resolution capability is achieved by curing the deposited resin using a UV laser. To ensure the isotropic properties of the printed parts, fluid flow simulations of the polymer resin through the designed dispensing system were performed. Therefore, the primary objectives were twofold: (1) designing a resin dispensing system including a specialized nozzle to promote laminar flow and (2) conducting fluent simulations to analyze the nozzle’s performance. The fluent simulations provided valuable insights into the fluid dynamics, affirming the nozzle’s efficiency in ensuring a consistent and controlled laminar flow of the resin during the dispensing process.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".