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Record W2021643179 · doi:10.1109/ccece.2008.4564706

Adhesive mechanical fastener design for use in microassembly

2008· article· en· W2021643179 on OpenAlexaffvenue
Lidai Wang, James K. Mills, William L. Cleghorn

Bibliographic record

VenueConference proceedings - Canadian Conference on Electrical and Computer Engineering · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFastenerAdhesiveMicromanipulatorMicroelectromechanical systemsMechanical jointMaterials scienceJoint (building)Computer scienceMechanical engineeringStructural engineeringEngineeringComposite materialNanotechnologyArtificial intelligence

Abstract

fetched live from OpenAlex

We present an adhesive mechanical fastener design used to construct three-dimensional micro devices. The fastener design includes adhesive bonding and self-alignment mechanisms. A micro probe that bonded to a robotic micromanipulator is employed to pick up and accurately deposit adhesive to a target location. Self-alignment mechanisms are introduced to increase the positioning accuracy. A curing light is applied to harden the adhesive. The cured adhesive keeps the assembled micropart into its position and provides a strong mechanical joint. By using conductive adhesive, a reliable electrical connection is achievable. The adhesive mechanical fastener only requires simple operations, and could reach reliable connections and high positioning accuracy, which is important to automatic microassembly. To demonstrate the feasibility of this method, many three-dimensional MEMS devices have been assembled, which include a three-dimensional rotary optical switch.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.038
GPT teacher head0.200
Teacher spread0.162 · 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 designBench or experimental
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

Citations4
Published2008
Admission routes2
Has abstractyes

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