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Record W4399142477 · doi:10.1002/mawe.202400013

Effect of Ni and Sr on the microstructure and tensile properties of the squeeze cast Al‐Si‐Cu alloy at elevated temperatures

2024· article· en· W4399142477 on OpenAlexaff
Fang Li, A. Hu, Wutian Shen, Henry Hu

Bibliographic record

VenueMaterialwissenschaft und Werkstofftechnik · 2024
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsMicrostructureAlloyUltimate tensile strengthMaterials scienceMetallurgyComposite material

Abstract

fetched live from OpenAlex

Abstract The influence of the transition alloying element nickel and the alkaline earth element strontium on the microstructure and tensile properties of squeeze cast Al−Si‐Cu alloy under as‐cast condition at elevated temperature of 100 °C, 200 °C and 300 °C is investigated in comparison with the conventional Al−Si‐Cu alloy (A380). Aluminum alloy A380 is alloyed and modified with 2 wt% additional nickel (Ni) and 0.02 wt% strontium (Sr). Squeeze casting is employed to cast the modified A380 under an applied pressure of 90 MPa. The results of tensile testing at the selected elevated temperatures indicate that the Ni and Sr containing A380 alloy exhibits a significantly improvement on tensile properties, specifically ultimate tensile strength and yield strength with 10 %‐30 % increases. The as‐cast microstructures of both the conventional and Ni and Sr‐containing alloys are observed by an optical microscope and further analyzed with scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The presence of Ni‐containing intermetallic phases with Ni addition, and the modified eutectic Si phase with refined morphologies due to the Sr addition should be responsible for the considerable enhancement on the as‐cast strengths of the squeeze cast Ni‐ and Sr‐containing alloy over those of the conventional A380 alloy.

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 score0.009
Threshold uncertainty score0.937

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 studies0.0000.001
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 head0.192
Teacher spread0.187 · 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
Published2024
Admission routes1
Has abstractyes

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