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Record W2168065962 · doi:10.1109/tepm.2009.2020515

Modeling of the Fluid Volume Transferred in Contact Dispensing Processes

2009· article· en· W2168065962 on OpenAlexaff
Daniel Chen, Mei-Lin Li, Tate Cao

Bibliographic record

VenueIEEE Transactions on Electronics Packaging Manufacturing · 2009
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsUniversity of Saskatchewan
FundersU.S. Department of Energy
KeywordsVolume (thermodynamics)Surface tensionMechanicsVolume of fluid methodLaplace's equationMaterials scienceFluid dynamicsProcess (computing)Control volumeMechanical engineeringBoundary value problemComputer scienceEngineeringMathematicsThermodynamicsFlow (mathematics)PhysicsMathematical analysis

Abstract

fetched live from OpenAlex

In the contact dispensing process, the contact of the fluid with the target board is essentially needed in order to transfer a certain volume of fluid to the board. Due to the action of surface tension, part of the fluid extruded from the needle hangs on the needle after the process, and this causes the difference between the fluid volume extruded and the one transferred to the board. This difference is usually ignored in the literature, yet is critical to the precise process control. In this paper, a model to represent the difference is developed based on the Young-Laplace capillarity equation as well as the boundary conditions established for this particular problem. Experiments and simulations were carried out to verify the model effectiveness as well as to investigate the influence of the fluid volume extruded from the needle, the needle size, and the initial height of the needle on the fluid volume transferred in the contact dispensing process.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.008
GPT teacher head0.199
Teacher spread0.190 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations15
Published2009
Admission routes1
Has abstractyes

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Same venueIEEE Transactions on Electronics Packaging ManufacturingSame topicNanomaterials and Printing TechnologiesFrench-language works237,207