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Record W2588117015

Droplet Deposition in Solid Ink Printing

2009· dissertation· en· W2588117015 on OpenAlexfundno aff
Ri Li

Bibliographic record

VenueTSpace (University of Toronto) · 2009
Typedissertation
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaXerox Foundation
KeywordsDeposition (geology)InkwellMaterials science3D printingNanotechnologyComposite materialGeologyGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

Introduced in 1991, solid ink color printing technology is widely used in the office printing, prepress proofing, and wide format color printing markets. Ink droplets are first deposited on a rotating drum and then transferred to paper to reproduce images with high print quality. The objective of this thesis is to develop scientific knowledge of ink droplet deposition, which is needed for precise image buildup on the drum surface.
\nThe first problem studied in the thesis is droplet formation from the printhead with varied working voltages and jetting frequencies. Attention is paid to the formation of satellite droplets, the contraction of ligaments and the startup of high frequency jetting. The jetting conditions for obtaining consistent droplet generation with satellites are determined. A theoretical model is developed to predict the lifetime of ligaments. 
\nThe second problem we studied is the deposition of single droplets on solid surfaces. The surface texture and final shape of deposited droplets are correlated with impact conditions, which include printhead temperature, substrate temperature, distance from printhead to substrate, and the type of substrate surface. An analytical model is developed to evaluate the interaction of oscillation and viscous damping in the droplet during impact.
\nThe third problem covered in the thesis is the deposition of multiple ink droplets on the drum surface. Interaction between droplets causes drawback effect, which degrades print quality. We define a parameter to quantify the drawback effect with varied deposition conditions. A simple model is provided to predict conditions for making continuous lines based on the results of two ink droplets deposition.
\nTo understand the hydrodynamics in causing drawback effect, a series of experiments using large liquid droplets are carried out. Focus is put on the evolution of spread length and dynamics of contact line. Correlations for maximum and minimum spread lengths are developed, which are used to reveal the cause of drawback effect in the deposition of ink droplets.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.984

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)0.0010.000

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 head0.007
GPT teacher head0.216
Teacher spread0.209 · 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 designObservational
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

Citations4
Published2009
Admission routes1
Has abstractyes

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