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Record W2060106663 · doi:10.1115/1.2721090

Modeling of Rotary Screw Fluid Dispensing Processes

2006· article· en· W2060106663 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.
fundA Canadian funder is recorded on the work.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.421
Threshold uncertainty score0.511

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)0.0000.000

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 head0.006
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