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Record W2171103287 · doi:10.1017/s0263574700002964

Development of a sliding-leg tripod as an add-on device for manufacturing

2001· article· en· W2171103287 on OpenAlexaff
Fengfeng Xi, Wanzhi Han, Marcel Verner, Andrew Ross

Bibliographic record

VenueRobotica · 2001
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsTripod (photography)WorkspaceKinematicsMachine toolCartesian coordinate systemInverse kinematicsComputer scienceFlexibility (engineering)Control engineeringRobotSimulationEngineeringMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents the work on developing a sliding-leg tripod as a programmable add-on device for manufacturing. The purpose is to enhance the capabilities of any machine by providing it with a more flexible range of motion. This device can be used as a toolhead for CNC machine tools and robots, or as a work stage for coordinate measuring machines and laser scanning systems. In this paper, system modelling, analysis and control of this device is presented. System modeling includes mobility study, kinematic model and inverse kinematics. System analysis includes workspace analysis, transmission ratio and stiffness analysis. System control includes path planning, joint space control and Cartesian space prediction. It is shown that the proposed device can provide flexibility and dexterity to machines.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.258
Teacher spread0.230 · 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
GenreMethods

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

Citations40
Published2001
Admission routes1
Has abstractyes

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