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Record W2904147303 · doi:10.1139/tcsme-2018-0097

Investigations on the principle of full decoupling and type synthesis of 2R1T and 2R parallel mechanisms

2018· article· en· W2904147303 on OpenAlexvenueno aff
Yundou Xu, Bei Wang, Zhifeng Wang, Yun Zhao, Wenlan Liu, Jiantao Yao, Yongsheng Zhao

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsDecoupling (probability)Rotation (mathematics)Rotation around a fixed axisMechanism (biology)Translation (biology)Control theory (sociology)Degrees of freedom (physics and chemistry)Type (biology)WrenchComputer sciencePhysicsTopology (electrical circuits)Classical mechanicsMathematicsEngineeringMechanical engineeringChemistryControl engineeringCombinatoricsArtificial intelligence

Abstract

fetched live from OpenAlex

Based on the relationship between constraint wrenches and rotational axes, the principle of full decoupling of two rotational degrees of freedom (DOFs) for a two-rotation and one-translation (2R1T) parallel mechanism and two-rotation (2R) parallel mechanism with three supporting branches is systematically analyzed. Two conditions for full decoupling of two rotational DOFs of such mechanisms are obtained. The relationship between the two rotational axes of the parallel mechanisms is classified into two cases: intersecting and different. Next, based on the two conditions, type synthesis of the 2R1T and 2R parallel mechanisms with fully decoupled two rotational DOFs is carried out. A series of novel 2R1T and 2R parallel mechanisms with fully decoupled two rotational DOFs are obtained, such as RPU–UPR–RPR. Several of these mechanisms contain only eight single-DOF passive joints, one fewer than in existing mechanisms of this type, and thus have broad applications.

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 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: none
Teacher disagreement score0.777
Threshold uncertainty score0.347

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 head0.014
GPT teacher head0.201
Teacher spread0.187 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations4
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicRobotic Mechanisms and DynamicsFrench-language works237,207