MétaCan
Menu
Back to cohort

Accelerated Aging and Portevin-Le Chatelier Effect in AA 2024

2016· article· en· W2550255951 on OpenAlexaff
Fabienne Delaunois, Edwin Denil, Y. Marchal, Véronique Vitry

Bibliographic record

VenueMaterials science forum · 2016
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsSonaca (Canada)
Fundersnot available
KeywordsPortevin–Le Chatelier effectMaterials scienceAluminiumAlloyMetallurgyAluminium alloyDuration (music)KineticsWork (physics)Process (computing)Dynamic strain agingMechanical engineeringComputer scienceEngineering

Abstract

fetched live from OpenAlex

AA2024 aluminium alloy is used in aeronautics for various applications. However, the aging treatment (“T4 temper”) at ambient temperature used to bring it to optimal mechanical properties causes several problems. The main one is its duration (5 days) which brings the need for storage of the parts. Moreover temperature control during such a long period is difficult.The aim of this work was to develop an alternative treatment leading to optimized mechanical properties in a shorter time. For this, we investigated the kinetics of the aging process and observed the apparition and disappearance of “Portevin-Le Chatelier (PLC) effect” in AA2024 aluminium alloy.The knowledge of the time at which PLC effect is observed is important in terms of process because shaping may be carried out before the end of the aging process.

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 score0.002
Threshold uncertainty score0.007

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)0.0020.000

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 head0.006
GPT teacher head0.207
Teacher spread0.201 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueMaterials science forumSame topicAluminum Alloy Microstructure PropertiesFrench-language works237,207