MétaCan
Menu
Back to cohort
Record W2158951173 · doi:10.1109/tmech.2005.848295

Modeling and Control of Dispensing Processes for Surface Mount Technology

2005· article· en· W2158951173 on OpenAlexaff
Daniel Chen, Greg Schoenau, Wenjun Zhang

Bibliographic record

VenueIEEE/ASME Transactions on Mechatronics · 2005
Typearticle
Languageen
FieldComputer Science
TopicWireless Sensor Networks for Data Analysis
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSurface-mount technologyConsistency (knowledge bases)CompressibilityMechanical engineeringProcess (computing)Computer scienceMaterials scienceProcess engineeringSolderingEngineeringComposite material

Abstract

fetched live from OpenAlex

Dispensing is a key process in surface mount technology (SMT), in which minute amounts of fluid materials (such as solder paste, adhesive) are delivered controllably onto printed circuit boards for the purpose of conducting, bonding, etc. Time-pressure dispensing by means of pressurized air is currently the most widely used approach in SMT. Due to air compressibility, the control of the time-pressure dispensing process has proven to be a challenging task in achieving a high degree of consistency in the amount of fluid dispensed. This paper presents the development of a model of the amount of fluid dispensed by taking air compressibility into account. Based on the model, a control strategy is then developed to improve the consistency in the amount of fluid dispensed. Experiments were conducted to verify the effectiveness of the 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.

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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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

Citations30
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueIEEE/ASME Transactions on MechatronicsSame topicWireless Sensor Networks for Data AnalysisFrench-language works237,207