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Bionic Design of the Surface Morphology of Rubber Bush Covered on Driving Drums

2010· article· en· W1642751446 on OpenAlexvenueno aff
Linjing Xiao, Xijing Wang, Quan Li

Bibliographic record

VenueAdvances in natural science/Advances in natural sciences · 2010
Typearticle
Languageen
FieldEngineering
TopicAdhesion, Friction, and Surface Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsDrumInterlockingBionicsFinite element methodMaterials scienceTraction (geology)Natural rubberMechanical engineeringEngineeringStructural engineeringComposite materialComputer science

Abstract

fetched live from OpenAlex

Driving drum uses friction force to transfer power in belt conveyor. By means of bionic technology, the surface morphology of driving drum's flexible cladding was researched to increase the frictional traction force in this paper. Taking tree frog and katydid as biological prototypes, the structural features and adhesion mechanisms of their epidermal pad attachment organs were studied. Imitating the shape and structure of the epidermal pads, based on the principle of function bionics, four new surface morphologies of drum’s bush were designed. The behavior of the bionic bush contacting to the belt was simulated with finite element analysis software. The results of contact analysis show that the bionic drum’s bushes can generate embedding and interlocking effect during the contact process. The contact form can be changed from plane or cambered surface contact to meshing contact to enhance the frictional traction of drums. Keywords: Epidermal pad; Surface morphology; Bionic design; Finite element analysis; Friction

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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.273
Teacher spread0.267 · 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

Citations2
Published2010
Admission routes1
Has abstractyes

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