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Record W2026326123 · doi:10.1115/1.4001244

Design of a Three-Axis Articulated Tool Head With Parallel Kinematics Achieving Desired Motion/Force Transmission Characteristics

2010· article· en· W2026326123 on OpenAlexaff
Xin-Jun Liu, Wang Li-ping, Fugui Xie, Ilian A. Bonev

Bibliographic record

VenueJournal of Manufacturing Science and Engineering · 2010
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsKinematicsMechanism (biology)Orientation (vector space)Revolute jointTransmission (telecommunications)Computer scienceMotion (physics)Head (geology)Control theory (sociology)SimulationGeometryComputer visionArtificial intelligencePhysicsMathematicsClassical mechanicsRobotGeologyTelecommunications

Abstract

fetched live from OpenAlex

This paper addresses the design issue of a three-axis tool head with three-PRS parallel kinematics (P, R, and S standing for prismatic, revolute, and spherical joint, respectively) by considering their orientation capability and motion/force transmission. The content presented here is actually an improvement on the dimensional optimization of articulated tool heads with parallel kinematics, with emphasis on the three-PRS design to solve the problem of orientational capability. An index that can evaluate the effectiveness of the motion/force transmission is introduced. The orientation capability with which the mechanism has high motion/force transmission capability is then defined. The procedure searching the link lengths with which the mechanism has a high orientation capability and good effectiveness of motion/force transmission is finally presented.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.194
Teacher spread0.182 · 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

Citations52
Published2010
Admission routes1
Has abstractyes

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