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Record W1972759240 · doi:10.1177/0954406211404910

Design and microfabrication of a constant-force microgripper

2011· article· en· W1972759240 on OpenAlexaff
MS Hajhashemi, Farshad Barazandeh, Saman Nazari Nejad, R Nadafi DB

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2011
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsUniversity of WaterlooSimon Fraser University
Fundersnot available
KeywordsMicrofabricationMaterials scienceProcess (computing)FabricationCharacterization (materials science)Mechanical engineeringPolyethylene terephthalateEtching (microfabrication)NanotechnologyComputer scienceComposite materialEngineering

Abstract

fetched live from OpenAlex

The characterization process of micro- and submicrometre particles in some cases demands their careful handling and placement. In most cases, a well-designed microgripper can address these requirements. The following research is focused on the process of design, finite element analysis, and microfabrication of an innovative compliant constant-force microgripper. This new architecture enables microgripper to handle microcomponents under constant gripping force without using any force control system. This characteristic makes it outstanding in compare to the previous works in literature. The adopted fabrication process is a ultraviolet-assisted vertical etching on polyethylene terephthalate substrate. This process is a well-established process to offer the requirements of high resolution and high aspect ratio in microfabrication of plastic structures. The prototyped sample has been successfully tested and its performance during micro-assembly process has been verified.

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)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.018
GPT teacher head0.211
Teacher spread0.192 · 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

Citations14
Published2011
Admission routes1
Has abstractyes

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Same venueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering ScienceSame topicAdvanced MEMS and NEMS TechnologiesFrench-language works237,207