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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 OpenAlex
Lidai Wang, James K. Mills, William L. Cleghorn

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.
venuePublished in a venue whose home country is Canada.

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.

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: Empirical
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.038
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