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Record W4283653589 · doi:10.3390/ma15134511

Effect of Zr and Ti Addition and Aging Treatment on the Microstructure and Tensile Properties of Al-2%Cu-Based Alloys

2022· article· en· W4283653589 on OpenAlexaff
E. Isaac Samuel, Ahmed Nabawy, A. M. Samuel, H. W. Doty, Victor Songméné, F. H. Samuel

Bibliographic record

VenueMaterials · 2022
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMicrostructureMaterials scienceUltimate tensile strengthMetallurgyComposite material

Abstract

fetched live from OpenAlex

The present work investigated the effect of aging treatment on the microstructure and tensile properties of an Al-2%Cu base alloy containing various additions of Zr and other alloying elements. Aging was carried out at temperatures of 180-300 °C for different aging times at each temperature. The tensile properties indicated that Zr additions improved the strength of the base alloy, especially at high Zr levels at 180 °C. At the 220 °C aging temperature, however, while Zr addition did not have a beneficial effect on the alloy strength, the ductility was found to improve. Zr-Ti combined additions had a significant effect on the microstructure of the base alloy, as the morphology of the α-Al grains transformed into a non-dendritic morphology, and the grain size decreased sharply. These effects were at their maximum at 180 °C and 0.5 wt% Zr addition. Moreover, the Zr-containing alloys aged at higher temperatures, such as 220 °C and 240 °C, maintained a noticeably higher level of strength over the base alloy aged at the same temperatures. Quality index charts based on the tensile test data also reflected an improvement in alloy quality and strength with Zr-Ti combined additions.

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 score0.373

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 head0.007
GPT teacher head0.183
Teacher spread0.176 · 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
Published2022
Admission routes1
Has abstractyes

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