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Record W2118309677 · doi:10.1017/s0263574702004083

Parallel kinematic machine design with kinetostatic model

2002· article· en· W2118309677 on OpenAlex

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.

Bibliographic record

VenueRobotica · 2002
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMechanism (biology)Flexibility (engineering)KinematicsControl theory (sociology)StiffnessComputer scienceGenetic algorithmBase (topology)Control engineeringLink (geometry)Compliant mechanismMachine toolEngineeringFinite element methodMechanical engineeringStructural engineeringMathematicsArtificial intelligenceControl (management)Physics

Abstract

fetched live from OpenAlex

In this paper, a new method – named lumped kinetostatic modeling – to analyze the effect of the link flexibility on the mechanism's stiffness is provided. A new type of mechanism whose degree of freedom (dof) is dependent on a passive constraining leg connecting the base and the platform is introduced and analyzed. With the proposed kinetostatic model, a significant effect of the link flexibility on the mechanism's precision has been demonstrated. The influence of the change in structure parameters, including material properties, on the system behavior is discussed. In the paper, the geometric model of this kind of mechanism is first introduced. Then, a lumped kinetostatic model is proposed in order to account for joint and link compliances; some results and design guidelines are obtained. Finally, the optimization of the precision is addressed using a genetic algorithm.

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 score0.161
Threshold uncertainty score0.691

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.025
GPT teacher head0.185
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