MétaCan
Menu
Back to cohort
Record W4285737293 · doi:10.18280/rcma.320303

Effect of Graphite Addition on Aluminum Hybrid Matrix Composite by Powder Metallurgy Method

2022· article· en· W4285737293 on OpenAlexvenueno aff
Muhammad Syahid, Azwar Hayat, Aswar Aswar

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2022
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsnot available
FundersUniversitas Hasanuddin
KeywordsMaterials scienceGraphiteComposite numberPorosityPowder metallurgyMicrostructureAluminiumComposite materialMetallurgy

Abstract

fetched live from OpenAlex

The requirement for optimal strength and lightweight materials is a challenge in today's automotive industry. In this study, the synthesis of aluminum matrix hybrid composites containing magnesium and copper with the addition of graphite 0, 1, 3, and 5% by volume was carried out using powder metallurgy techniques and investigated the effect of the treatment given. The investigation of composite properties was carried out by testing density, porosity, hardness, wear rate, XRD, and SEM investigations integrated with EDS. Optimal conditions indicated composite with a content of 5% volume of graphite with the lowest porosity level of 5.10%, the relative density of 88.67%, and the highest hardness at 50.37 HV, followed by superior wear resistance. This result is supported by observing secondary phase formation and microstructure through XRD and SEM-EDS. The addition of graphite in the composite supports optimizing physical and mechanical properties as a lightweight composite. It deserves to be considered as an alternative material for automotive components.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.262
Teacher spread0.246 · 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 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

Citations5
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueRevue des composites et des matériaux avancésSame topicAluminum Alloys Composites PropertiesFrench-language works237,207