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Record W3015783211 · doi:10.1088/2053-1591/ab871c

Abnormal grain growth during annealing in the Al/Al<sub>2</sub>O<sub>3</sub> composite produced by accumulative roll bonding

2020· article· en· W3015783211 on OpenAlexaff
M. Shamanian, Mohamad Reza Nasresfahani, Jerzy A. Szpunar

Bibliographic record

VenueMaterials Research Express · 2020
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAnnealing (glass)Materials scienceGrain boundaryComposite numberMicrostructureGrain growthComposite materialLattice (music)Abnormal grain growthGrain boundary strengtheningMetallurgyCrystallographyChemistry

Abstract

fetched live from OpenAlex

Abstract The mechanical and physical properties of composites are related to the matrix microstructure and reinforcements dispersion. Microstructural evolution during annealing can change the properties of the composites. Consequently, in this paper, the microstructural evolution of Al/Al2O3 composite during annealing and the interpretation of metallurgical phenomena were investigated. Accumulative roll bonding technique was used for manufacturing the Al/Al2O3 composite. The Al/Al2O3 composite specimens were further annealed at 375 °C for 15, 30 and 60 min without any protected atmosphere. The grain orientation (texture) and the grain size of the composites were investigated. Results showed that by increasing the annealing time to 60 min, abnormal grain growth started. Moreover, the frequency of low angle boundaries (1.5–5) increased, while the frequency of ∑3 Coincident Site Lattice boundaries decreased. In fact, grain boundaries with low Coincident Site Lattice were less prone to reinforcement pinning. Then low Coincident Site Lattice boundaries were more susceptible to formation and growth of abnormal grains.

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 score0.002

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)0.0000.000

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 head0.039
GPT teacher head0.278
Teacher spread0.239 · 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
Published2020
Admission routes1
Has abstractyes

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Same venueMaterials Research ExpressSame topicAluminum Alloys Composites PropertiesFrench-language works237,207