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Record W2012484016 · doi:10.1115/detc2005-85034

On Performance Enhancement of Parallel Kinematic Machine

2005· article· en· W2012484016 on OpenAlexaff
Dan Zhang, Lihui Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsNational Research Council CanadaOntario Tech University
Fundersnot available
KeywordsKinematicsComputer scienceTorqueDegrees of freedom (physics and chemistry)Kinematic chainMechanism (biology)MachiningMotion controlMachine toolKinematic diagramControl engineeringArtificial intelligenceEngineeringMechanical engineeringRobot

Abstract

fetched live from OpenAlex

This paper proposes a spatial three degrees of freedom parallel kinematic machine enhanced by a passive leg and a web-based remote control system. First, the geometric model of the three degrees of freedom parallel kinematic machine is addressed; in the mechanism, a fourth kinematic link — a passive link connecting the base center to the moving platform center — is introduced. This last link is used to constrain the motion of the tool (located in the moving platform) to only three degrees of freedom, as well as to enhance the global stiffness of the structure and distribute the torque from machining. With the kinematic model, a web-based remote control approach is then applied. The concept of the web-based remote manipulation approach is introduced and the principles behind the method are explored in detail. Finally, an example of remote manipulation is demonstrated to the proposed 3-DOF structure using web-based remote control concept before conclusions.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.191
Teacher spread0.185 · 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 designObservational
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

Citations0
Published2005
Admission routes1
Has abstractyes

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