MétaCan
Menu
Back to cohort
Record W2104730839 · doi:10.1109/ccece.2006.277563

Active Microgripper Interface Used in Microassembly of MEMS

2006· article· en· W2104730839 on OpenAlexaff
Yasser H. Anis, James K. Mills, William L. Cleghorn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInterface (matter)GrippersActuatorMicroelectromechanical systemsHingeMechanical engineeringComputer scienceInterchangeabilityMaterials scienceEngineeringNanotechnologyArtificial intelligence

Abstract

fetched live from OpenAlex

This paper describes the design of an active microgripper interface used in the assembly of three-dimensional MEMS (micro electromechanical systems) microstructures. The interface connects a robotic manipulator to a 3-pad active microgripper. Electric current is transmitted to the interface by bonding its pads to the robotic manipulator using standard tungsten probes. The interface is integrated with a hinged microstructure that is rotated by a 170deg angle causing the hinge plane to get in contact with the interface small pads. The interface, bonded to the robotic manipulator, is used to grasp the smaller active microgrippers. An interlocking mechanism is introduced to the interface to lock the microgripper and prevent it from sliding. The inverted hinged structure operates as a bridge that connects the microgripper pads to the robotic manipulator probes enabling the transmission of current. This allows the active microgripper to act as both an actuator and as a feedback sensor. The interface highly improves the interchangeability properties of the microgrippers, where replacing damaged microgrippers is easily performed and the setup time and effort would be significantly reduced. This paper is a part of an ongoing work which involves the automation of the assembly of MEMS, used to construct out-of-plane 3D microstructures

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 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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.296

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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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.

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
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced MEMS and NEMS TechnologiesFrench-language works237,207