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Record W2997489211 · doi:10.1080/02670836.2019.1705039

Enhanced mechanical properties of Al–Si–Cu–Mn–Fe alloys at elevated temperatures through grain refinement and dispersoid strengthening

2019· article· en· W2997489211 on OpenAlex
Haoyu Li, Bo Lin, Rui Xu, Kun Liu, Huaqiang Xiao, Yuliang Zhao

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

VenueMaterials Science and Technology · 2019
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
FundersPostdoctoral Research Foundation of ChinaGuizhou Science and Technology DepartmentNational Natural Science Foundation of China
KeywordsMaterials scienceMetallurgyMicrostructureAluminiumAlloyGrain sizeStrengthening mechanisms of materialsSolid solution strengtheningSolid solution

Abstract

fetched live from OpenAlex

The effect of Ti content on the microstructure and mechanical properties of heat-treated Al–Si–Cu–Mn–Fe alloys was investigated. It was found that the mechanical properties increased with the increase of Ti content. This was attributed to the refinement of grain size, the increased amount of T (Al 20 Cu 2 Mn 3 ), the α-Fe (Al 15 (FeMn) 3 (CuSi) 2 ) precipitated particles, and the decrease in Al 2 Cu. At an elevated temperature of 300°C, the heat-treated Al–Si–Cu–Mn–Fe alloy with 0.5% Ti demonstrated the best mechanical properties, which are superior to those of commercial aluminium alloys. The yield strength contribution at 300°C was quantitatively evaluated based on the dispersoid, solid solution, and matrix contributions. It was confirmed that the main strengthening mechanism in the experimental alloys was the dispersoid strengthening.

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 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 score0.606

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 head0.007
GPT teacher head0.199
Teacher spread0.192 · 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