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A Single-Parameter Model for Soft Bellows Actuators under Axial Deformation and Loading

2023· article· en· W4389666729 on OpenAlexaff
Emma Treadway, Melissa Brei, Audrey Sedal, R. Brent Gillespie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsBellowsActuatorRotary actuatorDeformation (meteorology)KinematicsControl theory (sociology)Nonlinear systemPneumatic actuatorMechanicsMechanical engineeringFluid powerMechanical energyMaterials sciencePower (physics)EngineeringComputer sciencePhysicsElectrical engineeringClassical mechanics

Abstract

fetched live from OpenAlex

Soft fluidic actuators are becoming popular for their backdrivability, potential for high power density, and their support for power supply through flexible tubes. Control and design of such actuators requires serviceable models that describe how they relate fluid pressure and flow to mechanical force and motion. We present a simple 2-port model of a bellows actuator that accounts for the relationships among fluid and mechanical variables imposed by the kinematics of the deforming bellows structure and accounts for elastic energy stored in the actuator's thermoplastic material structure. Elastic energy storage due to axial deformation is captured by revolving a differential strip whose linear elastic behavior is a nonlinear function of the actuator length. The model is evaluated through experiments in which either actuator length and pressure or force and pressure are imposed. The model has an error of 9.8% of the force range explored and yields insight into the effects of geometry changes. The resulting model can be used for model-based control or actuator design across the full operating range and can be exercised under either imposed force or imposed actuator length.

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: none
Teacher disagreement score0.740
Threshold uncertainty score0.233

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 head0.038
GPT teacher head0.246
Teacher spread0.207 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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