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Record W2038845409 · doi:10.1115/ht2005-72421

Experiments on Remelting and Solidification of Molten Metal Droplets Deposited in Vertical Columns

2005· article· en· W2038845409 on OpenAlexaff
Ming Fang, S. Chandra, Chul B. Park

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCoalescence (physics)Materials scienceSurface tensionDeposition (geology)MetalSubstrate (aquarium)Melting pointAluminiumLiquid metalComposite materialTinAnalytical Chemistry (journal)MetallurgyChemistryThermodynamicsChromatography

Abstract

fetched live from OpenAlex

Experiments were done to determine conditions under which vertical columns could be built by metal droplets landing sequentially on top of each other. Molten tin droplets (0.6 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: droplet temperature (250°C to 345°C), substrate temperature (60°C to 200°C) and deposition rate (1 Hz to 15 Hz). At lower deposition rates the substrate cooled down too much to induce remelting whereas at higher rates the tip of the column remained liquid and surface tension forces pulled it into a spherical mass. Assuming one-dimensional conductive heat transfer in a column a simple analytical model was developed to calculate the temperature at the tips of the column. It predicts that deposition frequency should be decreased as column height increases to hold the tip temperature constant. Droplet coalescence was best achieved when the tip temperature of a column was maintained at the melting point of the metal. Columns fabricated following the deposition frequency predicted by the model show good bonding between droplets and uniform diameter.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.157
Threshold uncertainty score0.259

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.243
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2005
Admission routes1
Has abstractyes

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