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Record W2910571958 · doi:10.1139/tcsme-2018-0016

Experimental and numerical study of a plastic worm meshed with a steel helical gear

2019· article· en· W2910571958 on OpenAlexvenueno aff
Dong Liang, Chuanshan Li, Chengli Hua, Tianhong Luo

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsnot available
FundersChongqing Municipal Education CommissionNational Natural Science Foundation of China
KeywordsWorm driveHobbingFinite element methodStructural engineeringTransmission (telecommunications)Mechanism (biology)Contact analysisMachiningProcess (computing)Materials scienceMechanical engineeringSpiral bevel gearEngineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

In this paper, the transmission mechanism of a plastic worm meshed with a steel helical gear is applied to achieve power transmission and motion transfer. Tooth profile equations of the gear pair are derived and general meshing conditions are proposed in terms of gear geometry. A mathematical model of the tooth profiles of the gear pair is established. Contact stress analysis and general evolution law of the tooth profiles are discussed using the finite element method. Material characteristics, mesh generation, contact definition, and constraint conditions are given. For comparison, analysis results of a steel worm meshed with a steel helical gear is also provided. Through the hobbing process, injection molding, and machining center, samples of steel helical gears, plastic worm, and steel worm are completed, respectively. A characteristic test of the transmission mechanism of the plastic worm meshed with a steel helical gear is carried out based on the microtransmission experimental platform. The contrasting results show that the plastic worm has better transmission performance.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.372

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 head0.005
GPT teacher head0.184
Teacher spread0.179 · 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
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
Published2019
Admission routes1
Has abstractyes

Explore more

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