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Record W4389523579 · doi:10.23977/jemm.2023.080408

Study on the Vibrational Effects of Gear Transmission Error in Automotive Differential

2023· article· en· W4389523579 on OpenAlexvenueno aff
Wei Li

Bibliographic record

VenueJournal of Engineering Mechanics and Machinery · 2023
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAutomotive industryAutomotive engineeringVibrationTransmission (telecommunications)EngineeringShock (circulatory)Differential (mechanical device)Noise (video)Computer scienceTelecommunications

Abstract

fetched live from OpenAlex

Vibration issues pose a significant challenge to the automotive transmission system, primarily caused by the gear transmission error in the differential gear. This vibration effect directly impacts vehicle performance and driving comfort, potentially leading to overall performance degradation, increased noise levels, reduced driving stability, and even health and safety concerns. To effectively address this challenge, researchers have implemented a series of vibration suppression and control strategies. These include improving gear manufacturing precision, utilizing shock absorbers, introducing active vibration control systems, applying intelligent control technology, and enhancing the suspension system. By implementing these strategies, a substantial improvement in the performance and safety of automobiles is expected, providing drivers with an exceptional driving experience. This study aims not only to address vibration issues but also to offer robust guidance for improving the design and performance of automotive transmission systems to meet increasingly stringent performance and comfort standards.

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 score0.069
Threshold uncertainty score0.263

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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