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Record W2060106663 · doi:10.1115/1.2721090

Modeling of Rotary Screw Fluid Dispensing Processes

2006· article· en· W2060106663 on OpenAlexafffund
Daniel Chen

Bibliographic record

VenueJournal of Electronic Packaging · 2006
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsMechanical engineeringVolumetric flow rateProcess (computing)Flow (mathematics)Materials scienceFluid dynamicsCutting fluidEngineeringMechanicsComputer scienceMachining

Abstract

fetched live from OpenAlex

Abstract Fluid dispensing is a process widely used in electronics packaging manufacturing, by which fluid materials are delivered in a controlled manner for the purpose of bonding, sealing, coating, or conducting. Among various dispensing approaches, the use of a motor-driven screw is recognized as one of the most promising approaches due to its capacity of achieving high flow rates without the need of refilling. In a dispensing process, the flow rate of fluid dispensed is critical to control the volume or amount of fluid dispensed. This paper presents the development of a model for the rotary screw dispensing process. By using the power law equation, the flow behavior of the fluid being dispensed is characterized and then, based on the fundamentals of flow in screw channels and circular tubes, a model is developed to represent the flow rate in the rotary screw dispensing process. Experiments and simulations were carried out to verify the model effectiveness as well as to investigate the performance of the rotary screw 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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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

Citations26
Published2006
Admission routes2
Has abstractyes

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