Building three‐dimensional objects by deposition of molten metal droplets
Bibliographic record
Abstract
Purpose The purpose of this paper is to determine conditions under which good metallurgical bonding was achieved in 3D objects formed by depositing tin droplets layer by layer. Design/methodology/approach Molten tin droplets (0.18‐0.75 mm diameter) were deposited using a pneumatic droplet generator on an aluminum substrate. The primary parameters varied in experiments were those found to most affect bonding between droplets on different layers: droplet temperature (varied from 250 to 325°C) and substrate temperature (varied from 100 to 190°C). Droplet generation frequency was kept low enough (1‐10 Hz) that each layer of droplets solidified and cooled down before another molten droplet impinged on it. Findings In this paper, a one dimensional heat transfer model was used to predict the minimum droplet and substrate temperatures required to remelt a thin layer of the substrate and ensure good bonding of impinging droplets. Cross‐sections through samples confirmed that increasing either the droplet temperature or the substrate temperature to the predicted remelting region produces good bonding between deposition layers. Originality/value This paper used a practical model to provide reasonable prediction of conditions for droplet fusion which is essential to droplet‐based manufacturing. The feasibility of fabricating 3D metal objects by deposition of molten metal droplets has been well demonstrated.
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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 | 0.001 | 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".