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Record W1995061642 · doi:10.1177/1045389x06064000

A Study on the Thermomechanical Properties of Shape Memory Alloys-based Actuators used in Artificial Muscles

2006· article· en· W1995061642 on OpenAlexaff
Aaron D. Price, Andrew Edgerton, Cedric Cocaud, Hani E. Naguib, Amor Jnifene

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2006
Typearticle
Languageen
FieldMaterials Science
TopicShape Memory Alloy Transformations
Canadian institutionsRoyal Military College of CanadaUniversity of TorontoUniversity of Ottawa
Fundersnot available
KeywordsShape-memory alloyActuatorSMA*Smart materialArtificial muscleMaterials scienceHysteresisRibbonMechanical engineeringFocus (optics)Computer scienceStructural engineeringArtificial intelligenceEngineeringNanotechnologyComposite materialPhysics

Abstract

fetched live from OpenAlex

Shape memory alloys (SMAs) are a class of smart material having the unique ability to return to a predefined shape when heated. These materials are employed in a variety of emerging applications, and may potentially be used to avoid traditionally voluminous and heavy prosthetic actuators. The primary focus of this article is to convey the design and evaluation of a compact, lightweight, and high-strain SMA ribbon-based artificial muscle for use in such biomimetic applications. A key factor in the design of such an actuator is a thorough understanding of the thermomechanical response of the shape memory material. As such, a review of the relevant constitutive models is included. A selected hysteresis model is evaluated for potential application to ribbon type elements. The proposed actuator achieves strains of 31.6%; a marked improvement over previously documented SMA-based actuators.

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 score0.003

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 science0.0000.000
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 head0.048
GPT teacher head0.260
Teacher spread0.212 · 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

Citations19
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Intelligent Material Systems and StructuresSame topicShape Memory Alloy TransformationsFrench-language works237,207