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Record W1980870528 · doi:10.1115/1.2756084

An Explicit Method for Determining the Force-Moment Capabilities of Redundantly Actuated Planar Parallel Manipulators

2006· article· en· W1980870528 on OpenAlexafffund
Alp Zibil, Flavio Firmani, Scott Nokleby, Ron P. Podhorodeski

Bibliographic record

VenueJournal of Mechanical Design · 2006
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsOntario Tech UniversityUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMoment (physics)WrenchActuatorRedundancy (engineering)Control theory (sociology)PlanarWorkspaceTorqueComputer scienceWork (physics)MathematicsEngineeringStructural engineeringMechanical engineeringRobotPhysicsClassical mechanicsArtificial intelligenceControl (management)

Abstract

fetched live from OpenAlex

A new explicit methodology for the determination of the force-moment capabilities of nonredundantly and redundantly actuated planar parallel manipulators (PPMs) is presented. This methodology is based on properly adjusting the actuator outputs to their maximum capabilities. As a result, the wrench to be applied or sustained is maximized. For a nonredundantly actuated PPM, one actuator can be maximized, while for a redundantly actuated PPM, one actuator, beyond the one of the nonredundant case, may be maximized for every degree of redundancy added to the mechanism. This methodology is compared to a previous work that required an optimization algorithm. The new method yields more accurate and reliable results and is considerably more efficient. Four studies of force-moment capabilities are considered: maximum force with prescribed moment, maximum applicable force, maximum moment with a prescribed force, and maximum applicable moment. The methodology is used to generate the force-moment capabilities of an existing PPM throughout its workspace.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.028
GPT teacher head0.267
Teacher spread0.239 · 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 designTheoretical or conceptual
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

Citations35
Published2006
Admission routes2
Has abstractyes

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