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Record W4318465265 · doi:10.3390/ma16031110

Effect of Si, Mn, Be and Sr Addition on the Tensile Properties of 6061 Type Alloys: Role of Aging Treatment

2023· article· en· W4318465265 on OpenAlexaff
A. M. Samuel, E. A. Elsharkawi, M. H. Abdelaziz, E. Isaac Samuel, F. H. Samuel

Bibliographic record

VenueMaterials · 2023
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsSaint Mary's UniversityUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsIntermetallicMaterials scienceAlloyUltimate tensile strengthMetallurgyQuenching (fluorescence)PrecipitationPhase (matter)Tensile testingCastingScanning electron microscopeComposite materialChemistry

Abstract

fetched live from OpenAlex

The present study was performed on a 6061-type alloy to examine the effects of minor additions (Si, Mn, Be, Sr) of the type of precipitated Fe-based intermetallics, in terms of Fe/Si ratios. All alloys were grain refined (0.15%Ti in the form of Al-5%Ti-1%B) to minimize hot tearing during casting. The effect of these intermetallics on the alloy tensile properties was also investigated. Tensile test bars were solutionized at 520 °C followed by quenching in warm water at 60 °C to avoid cracking. The quenched bars were aged at 175 °C for periods up to 100 h. Characterization of the formed intermetallics as well as phase precipitation were carried out using field emission scanning electron microscopy. In Be-treated alloys, α-Al8Fe2SiBe phase may precipitate along with α-Al15(Fe, Mn)3Si2 phase. In addition, Be results in fragmentation of the α-Fe phase when the alloy was Sr-modified, leading to better tensile properties, compared to those obtained from the base alloy under same conditions. It should be noted that this study does not promote the use of Be as it is a toxic element.

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 score0.001
Threshold uncertainty score0.261

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 head0.013
GPT teacher head0.201
Teacher spread0.188 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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