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Record W2022607927 · doi:10.1243/09544062jmes258

Kinetostatic Design of an Innovative Schönflies-Motion Generator

2006· article· en· W2022607927 on OpenAlexaff
Jorge Angeles, Stéphane Caro, Waseem Khan, Andrey Morozov

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science · 2006
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsSCARAGenerator (circuit theory)Base (topology)Rotation (mathematics)Motion (physics)Rigid bodyRobotDisplacement (psychology)Parallel manipulatorComputer scienceControl theory (sociology)Mechanism (biology)Translation (biology)EngineeringSimulationMathematicsPhysicsArtificial intelligenceClassical mechanicsMathematical analysisControl (management)

Abstract

fetched live from OpenAlex

In this paper, a novel parallel robot is introduced. The robot, a Schönflies-motion generator (SMG), is capable of a special class of motions, namely those produced with serial robots termed SCARA (selective-compliance assembly robot arm). These motions involve three independent translations and one rotation about an axis of fixed direction. Such motions are known to form a subgroup of the displacement group of rigid-body motions, termed the Schönflies subgroup. The SMG is composed of two identical four-degree-of-freedom serial chains in a parallel array, sharing one common base and one common moving platform. The proximal module of each chain is active and has two controlled axes, the motors being installed on the fixed base. The links can thus be made light, thereby allowing for higher operational speeds. The distal module, in turn, is passive and follows the motions of its active counterpart, the whole mechanism giving, as a result, a four-degree-of-freedom motion to its end platform.

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 score0.003
Threshold uncertainty score0.010

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 communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.012
GPT teacher head0.212
Teacher spread0.200 · 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

Citations87
Published2006
Admission routes1
Has abstractyes

Explore more

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