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Record W3216166815 · doi:10.4314/ijs.v23i2.15

Effect of magnesium on some mechanical properties of 1200 Aluminium alloy

2021· article· en· W3216166815 on OpenAlexaff
M. D. Shittu, Alake Olaniyi, A.A. Daniyan, Dayo Adeyemi Isadare, K. M. Oluwasegun, S. A. Ibitoye

Bibliographic record

VenueIfe Journal of Science · 2021
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloys Composites Properties
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMaterials scienceAluminium6063 aluminium alloyAlloyMetallurgyUltimate tensile strengthMagnesiumHardening (computing)5052 aluminium alloyDispersion (optics)Microstructure6111 aluminium alloy5005 aluminium alloyAluminium alloyComposite material

Abstract

fetched live from OpenAlex

This paper investigated the effect of magnesium as a dispersion strengthening material on some mechanical properties of 1200-Aluminium (Al-Fe-Si) alloy, a typical commercial aluminium alloy used in the production of household utensils. 1200-Aluminium (Al-Fe-Si) alloy containing varying percentages of the dispersion hardening material (i.e. Magnesium) were produced and mechanical tests namely; hardness, tensile strength and impact strength were carried out. Also, the microstructures of the cast materials were studied. The results showed that increase in magnesium content, as dispersion hardening material improved the hardness, tensile strength and caused a slight decrease in impact strength of the 1200-Aluminium alloy. It is inferred from this work that using magnesium as a dispersion hardening material brings about corresponding improvement in some mechanical properties of 1200-Aluminium (Al-Fe-Si) alloy.
 Keywords: Magnesium, Aluminium alloy, dispersion strengthened, reinforced alloy, crystallographic formation Depth, Geothermal Energy

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)0.001
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 score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.218
Teacher spread0.207 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIfe Journal of ScienceSame topicAluminum Alloys Composites PropertiesFrench-language works237,207