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Record W4412100141 · doi:10.18280/rcma.350320

Processing and Mechanical Properties of AA6061 Matrix Composites Reinforced With Nano Scaled Boron Nitride

2025· article· fr· W4412100141 on OpenAlexvenueno aff
Niveen Jamal Abdulkader, M. A. Abass, Mohanned M. H. AL-Khafaji, Ghufran Sh. Jassim

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2025
Typearticle
Languagefr
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsnot available
Fundersnot available
KeywordsBoron nitrideMaterials scienceComposite materialNano-BoronMatrix (chemical analysis)Chemistry

Abstract

fetched live from OpenAlex

In this research were investigated the mechanical characteristics and microstructural characterization of metal matrix composites (MMCs) reinforced with nanoscale boron nitride (BN) particles made of aluminum (Al6061).Al6061 was used as the basic matrix and nano boron nitride as the reinforcing phase in the stir casting technique used to create the composites.To examine its influence on the composite's behavior, the volume proportion of BN reinforcement was varied between 0% and 4%.This study's main goal was to assess how nano BN reinforcements affected the Al6061 matrix's microstructure and mechanical performance.Scanning Electron Microscopy (SEM) was used to undertake microstructural investigation in order to evaluate the phase distribution and dispersion of the reinforcing particles inside the matrix.Vickers microhardness tests, tensile strength measurements, and wear rate studies were all part of the mechanical characterisation process.Results showed that, according to SEM imaging, BN nanoparticles were evenly dispersed throughout the Al6061 matrix.As the amount of BN in the composites increased, so did their microhardness.In particular, with 4% BN reinforcement, the greatest microhardness value measured was 119 Vickers Hardness Number (VHN), which is a 28.81 improvement over the base alloy.The addition of reinforcements also increased the composite's tensile strength, which peaked at 341 MPa at 4% BN, or 13.89% higher than the original Al6061 alloy.These results demonstrate that adding nano boron nitride to Al6061 MMCs greatly improves their hardness and tensile strength.When compared to the unreinforced Al6061 matrix, the produced composites showed noticeably higher hardness and lower wear rates due to the nano boron nitride reinforcement.Significantly, the composite with 4 weight percent nano BN showed the best mechanical performance, suggesting that adding nano-scale reinforcements successfully increases the base alloy's durability and wear resistance.

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 head0.028
GPT teacher head0.246
Teacher spread0.218 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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