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Record W2626386766 · doi:10.4050/f-0070-2014-9630

Improving Wear and Fretting Characteristics with Fiber Reinforced Aluminum Liners

2014· article· en· W2626386766 on OpenAlexaff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsBell Helicopter Textron (Canada)
Fundersnot available
KeywordsMaterials scienceFrettingFretting wearAluminiumFiberComposite materialMetallurgy

Abstract

fetched live from OpenAlex

A study has recently been performed to investigate improvements between the interfaces of the transmission case and rolling element bearings that provides for extended wear life. This study included demonstration testing of the improvements shown in wear and fretting characteristics at the bearing/case interface by using a Fiber Reinforced Aluminum (FRA) liner. This liner material is an engineered metal matrix composite that exhibits the best mechanical properties of each of the constituent materials, and provides enhanced capabilities in terms of structural, thermal, and chemical performance. An additional benefit of using FRA liners is a reduced weight compared to traditional steel bearing liners. This weight improvement is achieved with little to no redesign work, and without increasing manufacturing complexity. With proper sizing, the FRA bearing liners are a direct replacement for the existing part. This paper recollects all the results of this recent study, as well as plans for the future.

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: Empirical
Teacher disagreement score0.982
Threshold uncertainty score0.275

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 head0.004
GPT teacher head0.169
Teacher spread0.165 · 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
Published2014
Admission routes1
Has abstractyes

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