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Record W4402989662 · doi:10.3390/ma17194805

Influence of Si on the Elevated-Temperature Mechanical and Creep Properties of Al–Cu 224 Cast Alloys during Thermal Exposure

2024· article· en· W4402989662 on OpenAlexafffund
Kun Liu, Zimeng Wang, Lei Pan, X.-Grant Chen

Bibliographic record

VenueMaterials · 2024
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsRio Tinto (Canada)Université du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceCreepThermalMetallurgyComposite materialThermodynamics

Abstract

fetched live from OpenAlex

The influence of Si content (0.1-0.8 wt.%) on the development of precipitation microstructures and the resultant mechanical and creep properties during thermal exposure, up to 1000 h at 300 °C, in Al-Cu 224 cast alloys, was systematically investigated. The room and elevated temperature yield strength (YS) increased with increasing Si content under the T7 condition, which was attributed to the fact that the Si promoted the precipitation of fine θ'. However, Si increased the coarsening of θ' during thermal exposure at 300 °C, and the alloys with low Si exhibited a higher YS and creep resistance at elevated temperatures than high Si alloys. The mechanical strength and creep resistance were mainly controlled by the precipitation strengthening of the predominant θ' phase. Because of the high mechanical strength and creep resistance of the 0.1Si alloy during long-term thermal exposure, the Si level in Al-Cu alloys should be maintained at a low level of 0.1 wt.% for high-temperature applications. The strengthening mechanisms were quantitatively analyzed, based on the characteristics of the precipitate. The predicted YS values under different exposure conditions agreed well with the experimentally measured values.

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 score0.002
Threshold uncertainty score0.487

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 head0.008
GPT teacher head0.181
Teacher spread0.173 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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