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Record W2186696467 · doi:10.1109/tepm.2003.820824

Off-line control of time-pressure dispensing processes for electronics packaging

2003· article· en· W2186696467 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 · 2003
Typearticle
Languageen
FieldEngineering
TopicInjection Molding Process and Properties
Canadian institutionsQueen's UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsElectronicsPressure controlElectronic packagingProcess controlProcess (computing)Computer scienceMechanical engineeringMaterials scienceEngineeringElectronic engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Fluid dispensing is one critical process in electronics packaging, in which fluid materials (such as encapsulant, adhesive) are delivered controllably onto substrates for the purpose of encapsulation. Time-pressure dispensing is recently the most widely used approach, and its control has proven to be a challenging task due to the fact that the dispensing process performance is significantly affected by the behavior of the fluid dispensed. Moreover, if the fluid exhibits time-dependent behavior, the control becomes more difficult and demanding. This paper presents a method to model the time-pressure dispensing process, taking into account the time-dependent fluid behavior. Based on the model developed, an off-line control strategy is developed for improving the process performance. Experiments on a typical commercial dispensing system were carried out to verify the effectiveness of the modeling method and the off-line control strategy.

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 score0.925
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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