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Record W2126178006 · doi:10.1115/detc2012-70878

The Kinematic Synthesis of a Robust RCCC Mechanism for Pick-and-Place Operations

2012· article· en· W2126178006 on OpenAlexafffund
Khalid Al-Widyan, Jorge Angeles

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsMcGill University
FundersHashemite UniversityNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsRobustness (evolution)KinematicsComputer scienceContext (archaeology)Selection (genetic algorithm)Mathematical optimizationArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

Proposed in this paper is a methodology to synthesize a RCCC four-bar linkage intended for pick-and-place operations. The synthesis problem is set in the context of rigid-body guidance, which can be solved exactly for up to four prescribed poses of the coupler link. As a consequence, for a pick-and-place operation, the selection of the unspecified two intermediate poses is thus left up to the mechanism designer’s judgment. In this paper, we propose a method to determine the two intermediate poses resorting to the concept of robustness. In fact, robustness is needed in this context to overcome the presence of uncertainty due to the selection of the two unspecified poses. To this end, a theoretical framework for model-based robust design is invoked and a general methodology for robust kinematic synthesis is laid down. A numerical example is included to validate the concepts and illustrate the application of the methodology proposed here.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.206
Teacher spread0.193 · 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

Citations4
Published2012
Admission routes2
Has abstractyes

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