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Record W2090768995 · doi:10.1117/12.524685

Tether and joint design for microcomponents used in microassembly of 3D microstructures

2004· article· en· W2090768995 on OpenAlexafffund
Nikolai Dechev, William L. Cleghorn, James K. Mills

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCMC Microsystems
KeywordsJoint (building)MicrostructureComputer scienceMaterials scienceGeologyEngineeringStructural engineeringMetallurgy

Abstract

fetched live from OpenAlex

A novel microassembly system has been developed to assemble surface micromachined micro-parts, into 3D microstructures. This work describes the tether and joint design of micro-parts used in the microassembly process. The process consists of (a) grasping a micro-part which is tethered to the substrate of a chip, (b) removing the micro-part from the substrate by breaking the tethers, (c) manipulating the micro-part from its original location of fabrication to the target assembly location, and (d) joining the micro-part to another micro-part. In this way, out-of-plane or in-plane microstructures can be assembled from a set of initially planar micro-parts. The tether design is an integral part of the grasping and removal process. The tethers provide restraint on the micro-parts while they are grasped by a passive, compliant microgripper, and are designed to break-away at pre-defined locations, after the grasping process. In addition, the tethers ensure that the micro-parts do not translate or rotate from their fabricated and released positions, during transportation of the carrier chip. The joint system used to join micro-parts together is called "snap-lock’ microassembly. It is based on the elastic deflection of a plug feature that forms an interference fit with a mating slot feature.

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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
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 head0.017
GPT teacher head0.222
Teacher spread0.205 · 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 teacher head, not a consensus.

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

Citations15
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdditive Manufacturing and 3D Printing TechnologiesFrench-language works237,207