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Effect of Hot Deformation on Microstructure and Mechanical Properties of Al-B<sub>4</sub>C Composite Containing Sc

2014· article· en· W2053346609 on OpenAlexafffund
Jian Qin, Zhan Zhang, X.‐G. Chen

Bibliographic record

VenueMaterials science forum · 2014
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite numberMicrostructureIndentation hardnessScandiumPrecipitationPrecipitation hardeningComposite materialHardening (computing)Solid solution strengtheningVickers hardness testStrengthening mechanisms of materialsOptical microscopeGrain sizeDeformation (meteorology)Scanning electron microscopeMetallurgy

Abstract

fetched live from OpenAlex

Scandium has been introduced into the Al-B4C composite to form Al3Sc precipitates which offer a significant strengthening effect and a good thermal stability of the mechanical properties at elevated temperatures. In the present study, the grain structure and Al3Sc precipitation of the hot-rolled Al-15vol.% B4C composite containing Sc were examined by optical and electron microscopes. The mechanical properties of the hot deformed composite were evaluated by means of Vickers microhardness measurements. The post heat treatment after hot rolling was conducted to obtain desirable mechanical properties. The hot-rolled Al-B4C composite containing Sc can yield a considerable precipitation hardening under an appropriate post heat treatment. Results show that some Sc could be consumed during high temperature solution treatments, which remarkably reduced the precipitation hardening of Al3Sc precipitates. The amount of Sc loss is associated with the deformation ratio and solution time.

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 score0.004

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 head0.005
GPT teacher head0.192
Teacher spread0.186 · 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

Citations3
Published2014
Admission routes2
Has abstractyes

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