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Record W4293094719 · doi:10.1109/aim52237.2022.9863264

Compliant Robotic Finger Design for One-Way Linear Actuators

2022· article· en· W4293094719 on OpenAlexaff
Arvin Tangestanian, Ryan Orszulik

Bibliographic record

Venue2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) · 2022
Typearticle
Languageen
FieldEngineering
TopicPiezoelectric Actuators and Control
Canadian institutionsYork University
Fundersnot available
KeywordsActuatorCompliant mechanismFinite element methodStiffnessTorqueStructural engineeringBeam (structure)Robot end effectorMechanism (biology)EngineeringFinger jointLinear actuatorComputer scienceRobotMaterials sciencePhysicsElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper investigates the design of a compliant robotic finger with beam flexure joints to act as antagonists against a known actuator load. Stiffness based flexure design equations are derived from a static analysis of the equivalent joint torques caused by end effector loading and the desired end effector pose. The beam flexure stiffnesses are then compared against a finite element analysis for a range of flexure lengths. Two 3D printed compliant finger prototypes are designed using this methodology and tested experimentally. The results indicate that the simple beam flexures have appropriate stiffnesses and the finger mechanism is able to achieve the desired pose at loads near the designed-for linear actuator forces.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.278
Teacher spread0.224 · 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.

Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations0
Published2022
Admission routes1
Has abstractyes

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