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Record W3008034734 · doi:10.1299/jsmedmc.2019.112

Theoretical and Experimental Evaluation on Specific Stiffness of a Integrally-Shaped Lightweight Honeycomb Robotic Arm

2019· article· en· W3008034734 on OpenAlexaboutno aff
Masanori Masuda, Yuya CHIKUI, Toru WATANABE

Bibliographic record

VenueThe Proceedings of the Dynamics & Design Conference · 2019
Typearticle
Languageen
FieldEngineering
TopicSoft Robotics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsIntegrally closedHoneycombHoneycomb structureStiffnessRobotic armComputer scienceStructural engineeringMaterials scienceMechanical engineeringEngineeringComposite material

Abstract

fetched live from OpenAlex

This paper proposes a novel robotic arm for astronautical development by using honeycomb structure. Space robotic arm should possess lightweight, highrigidity, and compact. Honeycomb structure that is used mainly for large panels is one of such lightweight and highrigidity material. In this study, Honeycomb structure is introduced to realize compact and lightweight arm. To avoid the influence of adherence, the experimental specimens are integrally shaped by using 3D printer. Theoretical and experimental evaluation of the integrally shaped honeycomb arm is carried out. Comparison with SRMS (Canada arm), the partial superiority of the presented honeycomb arm is confirmed. Besides, the integral shaping using 3D printer is confirmed to be effective to avoid the influence of adherence.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.018
GPT teacher head0.232
Teacher spread0.214 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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