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Record W2968236069 · doi:10.5267/j.esm.2019.7.001

The effects of adding silicon carbide SiCp on aluminum alloy and analyzing the fatigue parameters and mechanical properties for metal matrix composites

2019· article· en· W2968236069 on OpenAlexvenueno aff
Mazin Mahmood Yahya

Bibliographic record

VenueEngineering Solid Mechanics · 2019
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsnot available
FundersIndian Institute of Technology (BHU) Varanasi
KeywordsMaterials scienceSilicon carbideAlloyComposite materialAluminiumMetal matrix compositeCarbideSiliconMatrix (chemical analysis)MetalMetallurgy

Abstract

fetched live from OpenAlex

The low cycle fatigue (LCF) resistance of two different metal matrix composite (MMC) of AA6063 with 2% and 8% by volume silicon carbide (SiCp) particles having particulate size of 37 micron (400 mesh) at room temperature condition has been evaluated under fully reversed strain control testing. The influence of volume fraction (2 and 8 vol%) and strain ratio (R= -1) are examined. Increasing the content of SiCp results in the degradation of strain control fatigue properties while the transition fatigue life increases. Fatigued samples are examined using scanning electron microscopy in order to understand the failure mechanism (SEM). Microstructural features and failure mechanisms studied through scanning electron microscopy (SEM) confirmed low cycle fatigue failure nature.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.885

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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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 designBench or experimental
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
Published2019
Admission routes1
Has abstractyes

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