The effect of process condition of the ink-jet printing process on the molten metallic droplet formation through the analysis of fluid propagation direction
Bibliographic record
Abstract
This study investigated the droplet formation mechanism using simulation analysis under various process conditions of ink-jet printing. Experimentally, the droplet shapes were cataloged in four types with the dwell time of the mono-polar waveform, and used to verify the reliability of the numerical model by finding consistency with the simulated modes of droplet formation. Conventional droplet formation depiction using pressure variation has not been comprehensive; as such, fluid propagating velocity was used to discuss the droplet formation mechanism in this study. Through the analysis of the fluid propagation velocity, a sufficient outward momentum of the fluid at the nozzle and a pulling force to pinch off the liquid thread were found to be essential for generating a single droplet. An insufficient pulling force would lead to undesired droplet shapes. A bipolar waveform can be used to enhance the outward momentum and the pulling force of the fluid, and the improvements in droplet formation were revealed in both the simulation and experiment. Results demonstrate that the analysis from the velocity variation is a useful and essential method when evaluating the droplet formation mechanism.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".