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Record W4386082894 · doi:10.11159/icmie23.132

Bending Stress In Worm Gear Shafts Considering The Notch Effect And The Load Distribution

2023· article· en· W4386082894 on OpenAlexvenueno aff
Johannes Gründer, Alexander Monz

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsnot available
Fundersnot available
KeywordsWorm driveStress (linguistics)Structural engineeringLoad distributionBendingMaterials scienceComposite materialEngineeringSpiral bevel gear

Abstract

fetched live from OpenAlex

Worm gear units are helical gear units with an axis cross angle of 90. The load distribution on several tooth flanks enables the transmission of high torques. The worm shaft is made of case-hardened steel and the worm wheel of a bronze alloy to avoid scuffing in the tooth contact due to high temperatures in the contact area. Since the wheel is made of a softer material, the gear units usually fail due to damage of the wheel. Common causes of damage are wear, pitting or fracture of a wheel tooth or the entire rim. According to the state of the art, the worm shaft is mostly designed against deflection. In the further literature, cases of tooth breakage of worm shafts are also documented. By modifying the gear geometry or using higher strength materials for the worm wheel, the worm shafts may fail due to force or fatigue fracture under high loads. The accuracy of the gear assembly also has a significant impact on the load distribution of gear units. Deviations in the nominal positions of the components may cause load increases and accelerate the failure of the gear unit. To estimate the influence of the assembly deviations on the bending stress in the worm shaft, this paper presents an analytical calculation approach for determining the bending stress by considering the notch effect, the notch position and the load pattern and distribution. Finally, the bending stresses caused by various load patterns caused by assembly deviations are calculated and the effect is evaluated.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.196
Teacher spread0.191 · 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 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
Published2023
Admission routes1
Has abstractyes

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