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Record W2103214730 · doi:10.1109/tepm.2004.843083

Modeling of Positive-Displacement Fluid Dispensing Processes

2004· article· en· W2103214730 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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 As the volume of fluid decreases to the micro-liter range, however, this assumption is no longer valid because the fluid properties, in particular the compressibility and flow behavior, can have a significant influence on the volume of fluid dispensed.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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.939
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.010
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