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Record W1994600416 · doi:10.1115/1.2952821

Drawback During Deposition of Overlapping Molten Wax Droplets

2008· article· en· W1994600416 on OpenAlexafffund
Ri Li, Nasser Ashgriz, S. Chandra, John R. Andrews, James C. Williams

Bibliographic record

VenueJournal of Manufacturing Science and Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaXerox Foundation
KeywordsCoalescence (physics)Dimensionless quantityWaxMaterials scienceSurface tensionWeber numberMechanicsDrop (telecommunication)Deposition (geology)DrumDrop impactFlashingComposite materialThermodynamicsReynolds numberWettingTurbulenceMetallurgy

Abstract

fetched live from OpenAlex

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.

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 score0.368
Threshold uncertainty score0.666

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.008
GPT teacher head0.192
Teacher spread0.184 · 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

Citations33
Published2008
Admission routes2
Has abstractyes

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