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Record W4415096499 · doi:10.1002/aisy.202500668

Effective Material Stiffness in Curved Actuators

2025· article· en· W4415096499 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Intelligent Systems · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Materials and Mechanics
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsCurvatureActuatorStiffnessRadius of curvatureRADIUSStress (linguistics)Material properties

Abstract

fetched live from OpenAlex

This study presents a new method for measuring the effective stiffness of curved actuators. Actuators are loaded into tension, and analytical mechanical equilibrium formulations are used to determine the stress along the actuator. A new mechanical metric, Shape Actuation Modulus (SAM), defines the effective stiffness of the actuator during loading as the ratio of stress change to radius of curvature change. Conductive polylactic‐acid shape‐memory actuators are produced to benchmark this novel methodology. These actuators display a linear behavior between 25 and 50 mm radius of curvature with SAM of 3.8±0.9 MPa at 50 mm. The interval on which the radius of curvature to stress relationship is linear can be controlled by choosing the radius of curvature of the hinge. For instance, SAM calculation with R 2 > 0.97 was achieved in ranges of [22.7;79.6] mm and [16.4;51.5]mm for starting radius of curvature of 23.5±0.7 mm and 17.2±0.6 mm, respectively. Hence, the new technique proposed provides guidelines to design actuators. Finally, a comparison of bio‐composite actuators made of the same material was conducted. The hygromnemic actuators tested displayed a stiffness more than one order of magnitude larger than the hygromorphic ones for the range of radius of curvature [20;100]mm.

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.

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 score0.496
Threshold uncertainty score0.944

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 head0.005
GPT teacher head0.237
Teacher spread0.232 · 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