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Record W2016708867 · doi:10.1115/detc2008-49188

Novel Design of a 3-DOF Parallel Manipulator for Materials Handling

2008· article· en· W2016708867 on OpenAlexaff
Dan Zhang, Zhen Gao, Xiaolin Hu, Jason Parise

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsJacobian matrix and determinantWorkspaceKinematicsParallel manipulatorRigidity (electromagnetism)Computer scienceInverse kinematicsFinite element methodCADSingularityOptimal designDegrees of freedom (physics and chemistry)Control engineeringMechanical engineeringControl theory (sociology)EngineeringEngineering drawingMathematicsStructural engineeringArtificial intelligenceRobotApplied mathematics

Abstract

fetched live from OpenAlex

In this paper, a new design of a parallel manipulator is proposed for industrial applications, specifically for material surface finishing processes. Though most current parallel mechanisms have been based on the Stewart-Gough platform which has 6 degrees of freedom (DOF), the focus of this design is on a 3-DOF manipulator with one novel configuration. In order to benefit production, a parallel kinematic machine (PKM) capable of high speed industrial operations with high accuracy and rigidity is necessary. First, system modelling includes mobility study, inverse kinematic model, Jacobian matrix, singularity analysis and workspace calculation are conducted. Then, a CAD model is presented showing the optimum design features and detailed mechanics. Finally, finite element analysis is carried out for the device optimization.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.060
GPT teacher head0.221
Teacher spread0.160 · 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 designBench or experimental
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
Published2008
Admission routes1
Has abstractyes

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