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Record W2771257061 · doi:10.1109/sdpc.2017.62

A Comparison of Several Methods for the Calculation of Gear Mesh Stiffness

2017· article· en· W2771257061 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venue2017 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC) · 2017
Typearticle
Languageen
FieldEngineering
TopicGear and Bearing Dynamics Analysis
Canadian institutionsLaurentian UniversityQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsStiffnessComputationFinite element methodVibrationComputer scienceNoise (video)Structural engineeringDirect stiffness methodStiffness matrixAlgorithmEngineeringAcousticsPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

The time varying gear mesh stiffness is one of the main excitations that cause unwanted vibration and noise of gear transmission systems. Numerous methods for the calculation of gear mesh stiffness have been introduced by different researchers. This paper generalizes three commonly-used approaches that have been used in literature to yield gear mesh stiffness. They are based on finite element methods, analytical methods using the potential energy principle, and an approximation method based on the ISO standard. Systematic comparisons among them were made in terms of the mesh stiffness curves, corresponding order spectrum as well as the amount of time used for computation. The advantages and disadvantages of each method are summarized.

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.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.922
Threshold uncertainty score0.661

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
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 head0.057
GPT teacher head0.372
Teacher spread0.316 · 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