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Aluminium Metal Matrix Composite – An Insight into Solid State and Liquid State Processes

2015· article· en· W2241235107 on OpenAlexaff
K. R. Padmavathi, R. Ramakrishnan, K. Palani Kumar

Bibliographic record

VenueApplied Mechanics and Materials · 2015
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsSAIT Polytechnic
Fundersnot available
KeywordsMaterials scienceAluminiumComposite numberPowder metallurgyComposite materialBall millMetal matrix compositeReinforcementBall (mathematics)Thermal expansionMetallurgyModulusMicrostructure

Abstract

fetched live from OpenAlex

Aluminium metal matrix composites (AMMCs) are being considered as a group of superior material for its lightness, strength, high specific modulus, low coefficient of thermal expansion and good wear resistance properties. Solid state and liquid state processing methods are used to fabricate AMMCs. Achieving a uniform distribution of reinforcement within the matrix is one such challenge, which affects directly on the properties and quality of composite material. Powder metallurgy route, one of the solid state processing methods can be effectively used to get uniform dispersion of reinforcements with aluminium metal matrix. This paper presents the summary of the ball milling and stir casting processes to fabricate the AMMCs and its applications. Major issues like ball milling time, dispersion of reinforcements, grain size, the stirring time and speed are discussed. Also the effect of different reinforcement for AMMCs on the mechanical properties is discussed in detail.

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.227
Teacher spread0.213 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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