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Record W4389058309 · doi:10.3390/ma16237375

Effect of Aging Treatment on the Strength and Microstructure of 7075-Based Alloys Containing 2% Li and/or 0.12% Sc

2023· article· en· W4389058309 on OpenAlexaff
Ali Tahmasbi, Yasser Zedan, Victor Songméné, F. H. Samuel

Bibliographic record

VenueMaterials · 2023
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMicrostructureMaterials scienceMetallurgyComposite material

Abstract

fetched live from OpenAlex

The present study was performed on three versions of 7075 alloy to which Sc or Sc + Li was added. The alloys were subjected to various aging treatments. The microhardness results show that the highest value of hardness was achieved when the alloy containing Li + Sc was aged at 120 °C for 24 h whereas the minimum level was exhibited by the base alloy aged at 280 °C. The results were interpreted in terms of the size and distribution of the main hardening phase (η′(MgZn2)), and the role of the presence of Al and Cu in the used alloy. Precipitation of Al3(Sc, Zr, Ti) phase particles during solidification of the Sc-containing ingots was also discussed. The coarsening and spheroidi-zation of η-phase particles take place through the Ostwald ripening mechanism while smaller par-ticles in solution dissolve and deposit on larger particles. In Sc-containing alloys, star phase particle consists of different layers. The change in the brightness from layer to layer indicates that the Zr and Sc concentrations are varied within the star phase, since the atomic number of Zr (40) is higher than the atomic number of Sc (21). The addition of Sc, as well, leads to marked decrease in the grain size of the as-cast alloys i.e., 300 µm and 45 µm, respectively. The interaction between Li and Sc would reduce the effectiveness of the grain refining effect of Sc. The results of the refining effect of Sc were confirmed using the EBSD technique.

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 score0.452

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 head0.224
Teacher spread0.213 · 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

Citations9
Published2023
Admission routes1
Has abstractyes

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