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Record W2122748611 · doi:10.1109/icma.2005.1626842

Electro-thermo-dynamic performance of a microgripping system

2006· article· en· W2122748611 on OpenAlexaff
Evgueni V. Bordatchev, Marco J F Zeman, George K. Knopf

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsWestern UniversityNational Research Council Canada
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Microgripping systems incorporate miniature end grasping tools to manipulate micro-sized objects such as tiny mechanical parts, electrical components, biological cells, and bacterium. A thorough understanding of the system's dynamic behaviour, including the gripper force and tip displacement, is essential for successfully handling these micro-objects. In this paper, the electro-thermo-dynamic performance characteristics of a proposed microgripper are described. The system has a monolithic design which consists of a combination of an in-plane electro-thermally driven microactuator and a compliant mechanism. The kinematics of the microgripper is introduced and several prototypes are fabricated from 25/spl mu/m thick nickel foil. The dynamic and electro-thermal characteristics of the system are analyzed with respect to step responses, actuation/tweezing displacements, applied current/power, actual resistance and overall average temperature. Experiments demonstrate that the fabricated microgripper prototype with design parameters of /spl alpha//sub 0/ = 30/spl deg/, /spl beta//sub 0/ = 40/spl deg/, h/sub 0/ = 0/spl mu/m, and /spl lscr/ = 1.2 mm achieves tweezing displacement of 11.84/spl mu/m (tweezing gap of 23.68/spl mu/m) for an applied voltage and current of 1.41V and 0.15A, respectively. This experimental observation agrees with the predicted displacements from the kinematic model of 11.66 /spl mu/m. These preliminary results lay a foundation for developing micro grasping end-effectors for microrobotic and microassembly applications.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.237

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 head0.001
GPT teacher head0.165
Teacher spread0.163 · 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 designSimulation or modeling
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

Citations6
Published2006
Admission routes1
Has abstractyes

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