MétaCan
Menu
Back to cohort
Record W2736549019 · doi:10.4050/f-0073-2017-12149

Hybrid Ceramic Bearing Fatigue Testing for the Future Advanced Rotorcraft Drive System Program

2017· article· en· W2736549019 on OpenAlexaff
Cody Anderson, Lars Ponten, Jason Fetty

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicTribology and Lubrication Engineering
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsBearing (navigation)CeramicFatigue testingEngineeringAutomotive engineeringComputer scienceAerospace engineeringMechanical engineeringMaterials scienceStructural engineeringComposite materialArtificial intelligence

Abstract

fetched live from OpenAlex

Today's rotorcraft transmissions predominately utilize fully metallic bearings where both the raceways and rolling elements are metal. Ceramic bearing materials offer the potential for meeting the demand for weight reduction and increased power-to-weight ratios. Hybrid ceramic bearings incorporate both ceramic and metallic components forming an assembly that lends itself to improvements in weight, corrosion resistance, reduced friction, and improved surface characteristics. Typically, hybrid ceramic bearings consist of ceramic rolling elements and metallic raceways. Hybrid ceramic bearings have demonstrated feasibility for both remotely and non-remotely monitored applications in rotorcraft drive systems. Characterization of potential material combinations of hybrid ceramic bearings is needed to guide the design of hybrid ceramic bearings for use in future and modified rotorcraft transmissions. This research, conducted under the Future Advanced Rotorcraft Drive System (FARDS) program, examined multiple bearing material combinations and characterized them in fatigue testing. Hybrid ceramic bearing material combinations showed increased performance when compared to fully metallic bearing material combinations. Favorable results indicate that hybrid ceramic bearings can have immediate impact on rotorcraft transmission designs.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score0.455

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 studies0.0010.000
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 head0.017
GPT teacher head0.255
Teacher spread0.238 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicTribology and Lubrication EngineeringFrench-language works237,207