Drawback During Deposition of Overlapping Molten Wax Droplets
Bibliographic record
Abstract
Two overlapping droplets impacting on a solid surface coalesce and recoil so that the edges of the droplets are drawn back, a phenomenon called drawback. A series of experiments were conducted on the merging of two overlapping wax droplets deposited on an aluminum drum to characterize the drawback process between the two droplets. Drum temperature, droplet overlap ratio, and the time interval between impacts of droplets were varied. Wax bumps, formed by coalescence of two droplets on the drum surface, were photographed and their length and width measured. An aspect ratio and dimensionless drawback index, quantifying the extent of drawback, were calculated from these measurements. When drum temperature is increased, or the time interval between impacts of the two droplets is reduced, there is more drawback and the ink bumps become round, since the cooling rate of droplets is slower and droplets have a longer time to change shape due to surface tension. A simple heat transfer model was developed to predict changes in droplet-cooling rate with changes in droplet overlap, substrate temperature, or time interval (deposition frequency). Experiments were also conducted on the formation of lines by depositing 20 droplets. Measurements on the drawback of two droplets were used to predict conditions under which broken lines are obtained.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".