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Record W2779982439 · doi:10.5539/jmsr.v7n1p14

Synthesis and Characterization of Aluminium Base in situ Metal Matrix Composites by Spark Plasma Sintering

2017· article· en· W2779982439 on OpenAlexvenueno aff
B.P. Mallik, Koushik Sikdar, Debdas Roy

Bibliographic record

VenueJournal of Materials Science Research · 2017
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceSpark plasma sinteringComposite materialAluminiumVolume fractionSinteringComposite numberDifferential scanning calorimetryIndentation hardnessMetal matrix compositeAluminideScanning electron microscopeMicrostructureIntermetallic

Abstract

fetched live from OpenAlex

Fe-aluminide and alumina reinforced in-situ aluminium based metal matrix composite was prepared by spark plasma sintering (SPS) of aluminium and nanosized Fe2O3 powder mixture. In-situ reinforcements were formed during SPS by exothermal reaction between aluminium and nano-size Fe2O3 particle. The thermal characteristics of the in-situ reaction were studied by differential scanning calorimetry (DSC). Field Emission Scanning Electron Microscopy (FESEM) along with the Energy Dispersive Spectroscopy (EDS) and X-ray diffraction (XRD) techniques were used to study the microstructural architecture of the composites as a function of SPS temperature and the volume fraction of reinforcement. Microhardness measurement of the composite shows significant increase in hardness with increase in SPS temperature and volume fraction of secondary phase.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.042
GPT teacher head0.314
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2017
Admission routes1
Has abstractyes

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Same venueJournal of Materials Science ResearchSame topicAluminum Alloys Composites PropertiesFrench-language works237,207