MétaCan
Menu
Back to cohort
Record W3203995879 · doi:10.1515/ijmr-2021-8283

Effects of Zr and Sc additions on precipitation of α-Al(FeMn)Si dispersoids under various heat treatments in Al–Mg–Si AA6082 alloys

2021· article· en· W3203995879 on OpenAlexaff
Kun Liu, E. M. Elgallad, Chen Li, X.-Grant. Chen

Bibliographic record

VenueInternational Journal of Materials Research (formerly Zeitschrift fuer Metallkunde) · 2021
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMaterials scienceAlloyPrecipitation hardeningMetallurgyPrecipitationIndentation hardnessHardening (computing)Solid solution strengtheningZirconiumMicrostructureComposite material

Abstract

fetched live from OpenAlex

Abstract The present work investigated the influence of Zr and Sc on the evolution of α-Al(FeMn)Si dispersoids (“α-dispersoids") in Al–Mg–Si alloys. Both the individual addition of Zr and the combined additions of Sc and Zr increased the size but decreased the number density of the α-dispersoids, indicating the reduction in the formation of α-dispersoids. However, the reduction levels were the most significant when heat-treated at 350 °C in the alloy with both Sc and Zr and at 400 ° C in the alloy with only Zr, which were likely related to the different interactions between intermediate B’ precipitates and α-dispersoids with the addition of Zr and Sc. Although the α-dispersoids were suppressed in the Zr/Sc-containing alloys, their microhardness was generally higher than the base alloy, which can be attributed to the strengthening contribution induced by Zr and Sc either from their solid solution hardening or the precipitation hardening of Al 3 Zr/Al 3 (Sc, Zr) dispersoids.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.036
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.306
Teacher spread0.286 · 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 teacher head, 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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Materials Research (formerly Zeitschrift fuer Metallkunde)Same topicAluminum Alloy Microstructure PropertiesFrench-language works237,207