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Record W4384697527 · doi:10.1115/1.4062984

Dynamics of a Parallel-Kinematics Machine With Six Pairs of Offset Joints

2023· article· en· W4384697527 on OpenAlex
Hasiaoqier Han, Jorge Angeles

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Mechanisms and Robotics · 2023
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsMcGill University
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsKinematicsWrenchOffset (computer science)Kinematic chainControl theory (sociology)Screw theoryComputer scienceTwistActuatorKinematics equationsMathematicsRobot kinematicsEngineeringStructural engineeringPhysicsGeometryClassical mechanicsRobotArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract The authors propose a systematic formulation of the dynamics of the 6-P-RR-R-RR parallel-kinematics machine (PKM) with offset RR -joints. The kinematics of the same system is reported in an accompanying paper. Based on the kinematics model developed in the former, the dynamics model of the limb-chain is derived here using the Newton–Euler equations. Then, the constraint wrenches in the governing equations of the limb-chain are eliminated with the aid of the natural orthogonal complement. This is the twist-shaping matrix, which maps the joint-rate array of the limb-chain into the twist array of the PKM. Furthermore, the dynamics model of the whole PKM with offset joints is formulated. Moreover, the actuator forces are obtained. Finally, upon validation via simulation, the dynamics model is proven to be both precise and effective.

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.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.151
Threshold uncertainty score0.598

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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