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Record W2103214730 · doi:10.1109/tepm.2004.843083

Modeling of Positive-Displacement Fluid Dispensing Processes

2004· article· en· W2103214730 on OpenAlexaff
Daniel Chen, J. Kai

Bibliographic record

VenueIEEE Transactions on Electronics Packaging Manufacturing · 2004
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsPiston (optics)Displacement (psychology)Fluid motionFluid dynamicsCompressibilityVolume (thermodynamics)Positive displacement meterMechanicsMaterials scienceWork (physics)Volume of fluid methodMechanical engineeringFlow (mathematics)EngineeringThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Dispensing has been widely used in industry to precisely control the delivery of fluid materials. Among several approaches, positive-displacement dispensing by employing the linear movement of a motor-driven piston is recognized as the most promising because the approach is assumed to be "true" volumetric dispensing. This is true for dispensing a large volume of fluid with continuous piston motion. As the volume of fluid decreases to the micro-liter range, however, this assumption is no longer valid because the fluid properties, in particular the compressibility and flow behavior, can have a significant influence on the volume of fluid dispensed. Taking into account the influence, This work presents the development of a model for the positive-displacement dispensing process. The model is then used to investigate the process performance, with the emphasis on identifying the influence of such factors as the fluid volume in a syringe, the needle temperature, and the fluid properties on the consistency in volume of fluid dispensed.

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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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

Citations67
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Electronics Packaging ManufacturingSame topicAdditive Manufacturing and 3D Printing TechnologiesFrench-language works237,207