Numerical study on structures formed by the deposition and solidification of a single molten droplet
Bibliographic record
Abstract
The formed structure is of importance in determining the surface quality of a component made by droplet-based 3D printing. In the present work, the molten droplet solicitation process was simulated under an axisymmetric system where the smallest length scale and time scale were fully resolved. Evolutions of sharp droplet interfaces were captured through the front tracking method. Parametric studies have been carried out to explore how the dynamic metrics, which include the Ohnesorge number (Oh) and Weber number (We), can affect the structure of depositing droplet. The effect of the superheat parameter on the cooling rate was also investigated in the final section. Numerical results show that the inertial resisting force is critical dynamics in the variation of horizontal dimensionless length at the early deposition process. Three levels of Oh numbers and stages of We numbers were classified according to the deformation behavior. Flattening degree under different Oh number and We number cases were both fitted well by the exponential function. This paper also reveals that the variation law of the cooling rate and solidification time is affected by the superheat parameter, resulting in a feasible and promising method to predict droplet deformation time through the fully resolved numerical simulations during the manufacturing 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".