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Record W1993108722 · doi:10.1117/12.2086799

Variable stiffness structure using nylon actuators arranged in a pennate muscle configuration

2015· article· en· W1993108722 on OpenAlexaff
Soheil Kianzad, Milind Pandit, Johnathan D. Lewis, Alexander R. Berlingeri, Karl J. Haebler, John D. W. Madden

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsStiffnessMaterials scienceActuatorElectrical impedanceOpticsComposite materialComputer sciencePhysics

Abstract

fetched live from OpenAlex

In muscle variable impedance and ability to recruit fibers as needed helps enable actions such as walking and catching. A new biomimetic structure of nylon actuator is presented that imitates the human pennate muscle in structure, ability to vary stiffness and the ability to increase force by recruiting additional fibers. The actuator consists of 16 silver coated nylon coiled fibers attached to a central tendon at an angle of 20°. Each nylon coil produces 20 MPa of stress at constant length and nearly 20% strain at fixed load. Fibers are individually switched ON and OFF using transistors so that each element can be recruited, and the stiffness varied. The amount of input power is controlled with pulse width modulation (PWM) techniques. It is observed that the spring constant of the pennate structure varies from that of its passive state, 503 N/m and a resonance frequency of 1.4 Hz, to 1480 N/m with resonance frequency of 3.1 Hz in the active state where all the fibers are switched on under a 25 N load. Stiffness can be varied by a factor of 9.

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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

Citations28
Published2015
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicAdvanced Sensor and Energy Harvesting MaterialsFrench-language works237,207