Heat Transfer During Deposition of Molten Aluminum Alloy Droplets to Build Vertical Columns
Bibliographic record
Abstract
To create functional metal parts by depositing molten metal droplets on top of each other, we have to obtain good metallurgical bonding between droplets. To investigate conditions under which such bonds are achieved, experiments were conducted in which vertical columns were formed by depositing molten aluminum alloy (A380) droplets on top of each other. A pneumatic droplet generator was used to create uniform, 0.8 mm diameter, molten aluminum droplets. The droplet generator was mounted on a stepper motor and moved constantly so as to maintain a fixed distance between the generator nozzle and the tip of the column being formed. The primary parameters varied in experiments were those found to have the strongest effect on bonding between droplets: substrate temperature (250–450°C) and deposition rate (1–8 Hz). Droplet temperature was constant at 620°C. To achieve metallurgical bonding between droplets, the tip temperature of the column should be maintained slightly below the melting temperature of the alloy to ensure remelting under an impacting drop and good bonding. The temperature cannot exceed the melting point of the metal; otherwise the column tip melts down. The temperature at the bottom of a column was measured while droplets were being deposited. An analytical one-dimensional heat conduction model was developed to obtain the transient temperature profile of the column, assuming the column and the substrate to be a semi-infinite body exposed to a periodic heat flux. From the model, the droplet deposition frequency required to maintain the tip temperature at the melting point of the metal was calculated.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".