Theoretical Design of Inkjet Process to Improve Delivery Efficiency
Bibliographic record
Abstract
Inkjet technology is an essential tool for precise and quick delivery of liquids in micro-droplets. A key topic of the technology is to deliver the droplets efficiently by designing the nozzle that is related to the droplet speed and the droplet volume in a stable inkjet process. The ejected droplets are usually too small to determine their physical states through onsite measurement. Complex physical phenomena, such as the coupling effects of surface tension, viscous force and inertial force, make it difficult to optimize the nozzle design by experiments alone. In the paper, we adopt computational fluid dynamics to investigate the inkjet process with the orthogonal test method to arrange the studied cases. The computational results firstly have been verified through measuring a simulated case that could be observed in the experiment. Different nozzle structures then have been examined by numerical simulation. It is found that the Laval-shaped nozzle can improve the droplet speed significantly to deliver the droplets fast, and that the curvilinear-triangle-shaped nozzle can minimize the droplet volume to improve the printing accuracy. It is further revealed that a large ink viscosity and surface tension, as well as a low ink density can improve the process stability. Additionally, a parameter combined by the droplet speed, the droplet volume and the stability level is proposed to evaluate the comprehensive performance of the inkjet nozzle.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".