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Record W2077850336 · doi:10.1179/026708304225012053

Effect of cooling rate on solidification characteristics of aluminium alloy AA 5182

2004· article· en· W2077850336 on OpenAlexafffund
S. Thompson, Steve Cockcroft, Mary A. Wells

Bibliographic record

VenueMaterials Science and Technology · 2004
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversity of British Columbia
FundersUniversity of Windsor
KeywordsEutectic systemMaterials scienceSolidusAlloyPrecipitationAluminiumCooling curveMetallurgyFraction (chemistry)MicrostructureAnalytical Chemistry (journal)ThermodynamicsChromatographyChemistry

Abstract

fetched live from OpenAlex

Microstructural reactions during solidification of aluminium alloy AA 5182 were investigated using cooling curve analysis to determine the start temperature of the various transformations and the overall evolution in solid fraction. Three cooling rates were studied: 0.5 K s -1 , 1 K s -1 and 2 K s -1 . The key finding of the study is that the main eutectic reaction and subsequent reactions, including Mg 2 Si formation, occur at much lower temperatures and higher solid fractions than previously published. The primary eutectic was found to precipitate between 575 and 588 °C, which corresponds to a solid fraction between 0.87 and 0.91, and Mg 2 Si was found to precipitate between 551 and 560 °C, which corresponds to a solid fraction between 0.96 and 0.97. Increasing cooling rate was observed to result in a slight increase in solid fraction for primary eutectic precipitation from 0.87 - 0.88 at the low cooling rate to 0.89 - 0.91 at the intermediate cooling rate and 0.91 at the highest cooling rate. The highest cooling rate also resulted in a drop in solidus temperature to 461 °C from 500 - 510 °C (at the low and intermediate cooling rates) that led to an increase in the solidification interval from 123 K to 151 K.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.207
Teacher spread0.202 · 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

Citations23
Published2004
Admission routes2
Has abstractyes

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