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Record W4412426637 · doi:10.1016/j.matdes.2025.114373

Investigation of the high thermal ageing resistance of the 2219 aluminium alloy

2025· article· en· W4412426637 on OpenAlexaff
Tess E. Perrin, Arthur Després, Pierre Heugue, A. Deschamps, Frédéric De Geuser

Bibliographic record

VenueMaterials & Design · 2025
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsSafran Electronics (Canada)
FundersDirection générale de l'aviation civileMinistère de l'Économie, des Finances et de l'IndustrieAgence Nationale de la Recherche
KeywordsMaterials scienceAluminiumMetallurgyAlloyAgeingAluminium alloy5005 aluminium alloyThermal6111 aluminium alloy

Abstract

fetched live from OpenAlex

The 2219 aluminium alloy is widely used in high-temperature applications, particularly in aerospace. Thermal ageing during duty can lead to the coarsening of θ’ hardening precipitates, which are responsible for the mechanical properties of the alloy. In this study, temperature gradient heat treatments were employed to evaluate its resistance to thermal ageing over a continuous temperature range of 165 °C to 245 °C for up to 10,000 h. Despite its composition being primarily that of an Al-Cu binary alloy, hardness mappings revealed a limited decrease in mechanical properties. Microstructural characterisation using high-throughput scattering techniques (SAXS & WAXS) showed that this stability is due to a remarkable resistance of the θ’ precipitates to coarsening and limited θ’ to θ transformation, as validated by a precipitate hardening model. This stability suggests that minor alloying elements play a crucial role in thermal resistance. Atom probe tomography (APT) analyses showed that this stability may arise from mechanisms similar to those observed in recently developed ACMZ alloys, as Mn segregations were observed at the interfaces of the θ’ precipitates. However, unlike ACMZ alloys, at 300 °C, the stabilising mechanism is insufficient to preserve mechanical properties, suggesting a strong sensitivity of these mechanisms to compositional variations.

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)0.000
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 score0.026
Threshold uncertainty score0.319

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 head0.011
GPT teacher head0.178
Teacher spread0.167 · 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

Citations7
Published2025
Admission routes1
Has abstractyes

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