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Record W4294975780 · doi:10.1002/adem.202200973

Two‐Step Preaging of an Al–Mg–Si Alloy

2022· article· en· W4294975780 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Engineering Materials · 2022
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsNovelis (Canada)
Fundersnot available
KeywordsMaterials scienceAlloyHardening (computing)Two stepCluster (spacecraft)AluminiumMetallurgyChemical engineeringComposite materialChemistryCombinatorial chemistry

Abstract

fetched live from OpenAlex

Preaging (PA) is an industrial routine that suppresses the deleterious effect of natural aging and enhances the final paint‐bake hardening of 6XXX aluminum alloys. In view of the different advantages of PA at high and low temperatures, the effects of performing PA in two steps at different temperatures, namely, 80 and 160 °C, are explored. Various two‐step PA combinations are investigated, involving both the temperature orders and various aging times at both temperatures, while aiming at the same hardness after all PA treatments. These results are interpreted on the basis of cluster formation and vacancy evolution during two‐step PA. In particular, the first PA step is found to play a more important role than suggested by the durations of the two PA steps as the clusters formed in the first step can strongly influence the subsequent evolution of both clusters and vacancies in the second step. It is found that two‐step PA results in a compromise between the effects of one‐step PA at both temperatures, that is, the enhancement of natural secondary aging stability is accompanied by a reduced paint‐bake hardening or vice versa.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.368
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 head0.004
GPT teacher head0.195
Teacher spread0.191 · 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