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 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/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.

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: Methods · Consensus signal: none
Teacher disagreement score0.799
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
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