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Record W4200496404 · doi:10.37775/eis.2021.2.4

Wear analysis of natural-inorganic fiber reinforced automotive brake composites

2021· article· en· W4200496404 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.

Bibliographic record

VenueMérnöki és Informatikai Megoldások · 2021
Typearticle
Languageen
FieldEngineering
TopicBrake Systems and Friction Analysis
Canadian institutionsSavaria (Canada)
Fundersnot available
KeywordsMaterials scienceTaguchi methodsComposite materialBrake padComposite numberBrakeCompression moldingMolding (decorative)FiberDisc brakeOrthogonal arrayMoldMetallurgy

Abstract

fetched live from OpenAlex

Brake friction composite materials comprising varying proportions of natural (banana) and inorganic (lapinus) fibers were designed, fabricated by compression molding, and characterized for sliding wear performance. The sliding wear properties of the manufactured friction composites have been studied by the Taguchi method. An orthogonal array (L 16) was used to investigate the influence of sliding wear parameters. A series of tests were conducted on a pin-on-disc machine by considering four control parameters: composition, normal load, sliding velocity, and sliding distance, each having four levels. The results showed that the wear in terms of weight loss decreases with increasing banana fiber and increases with increasing lapinus fiber, normal load, sliding velocity, and sliding distance. The results indicate that the normal load emerges as the most significant control parameter affecting wear performance, followed by sliding distance and sliding velocity.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.235
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.192
Teacher spread0.187 · 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