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Record W1586699141 · doi:10.1002/9780470294437.ch35

Evaluation of Fibre-Matrix Interfacial Strength in a SiC Fibre-Reinforced Ti-6A1–4V Composite

2008· book-chapter· en· W1586699141 on OpenAlexaff
R. Berriche, Prateek Saxena, A. K. Koul, J. Beddoes

Bibliographic record

VenueCeramic engineering and science proceedings · 2008
Typebook-chapter
Languageen
FieldEngineering
TopicAdvanced Surface Polishing Techniques
Canadian institutionsCarleton UniversityNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceComposite materialComposite numberScanning electron microscopeHot isostatic pressingDrop (telecommunication)IndentationDisplacement (psychology)Microstructure

Abstract

fetched live from OpenAlex

A high resolution depth sensing indentation instrument, developed at NRC, called the Nanomechanical Probe, was used to conduct fibre push-out (FPO) tests on a SiC fibre-reinforced Ti-6A1–4V composite, produced by a spray method. Samples were tested in the as-fabricated state, after heat treatment and after hot isostatic pressing (HIPing). In all cases, load-displacement plots obtained from the FPO tests showed an initial linear increase in the load with displacement which was followed by a sudden drop of the load. This drop was attributed to failure of the fibre-matrix interface and the start of sliding of the fibre out of the matrix. Scanning electron microscopic examination of the samples after the test showed tested fibres protruding from the sample as a result of sliding during the FPO test. The maximum load values, obtained before the sudden drop, were used in a model to calculate the fibre-matrix interfacial strength. The results indicated that the interfacial strength of the HIPed sample was more than five times higher than that of the as-fabricated sample. The heat treatment, on the other hand, was found to reduce the strength by a factor of two as compared to the as-fabricated material.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.254
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.252
Teacher spread0.236 · 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.

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
Published2008
Admission routes1
Has abstractyes

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