MétaCan
Menu
Back to cohort
Record W4245271153 · doi:10.32920/ryerson.14664900.v1

Inclusion removal and grain refinement of magnesium alloy castings

2021· preprint· en· W4245271153 on OpenAlexaff
Abdallah Elsayed

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceMetallurgyFoundryUltimate tensile strengthMagnesiumCastingAlloyCarbon fibersCarbideGrain sizeSilicon carbideComposite materialComposite number

Abstract

fetched live from OpenAlex

Magnesium alloys show promise to be materials for lightweighting of automotive and aerospace vehicles improving fuel efficiencies and vehicle performance. A majority of magnesium alloy components are produced using casting where susceptibility to forming inclusions and coarse grain sizes could result. Development of effective inclusion removal techniques and better understanding of grain refinement of magnesium alloys could help in improving their mechanical properties to advance them to more structurally demanding applications. This research aimed to develop an environmentally friendly alternative to the grain refinement and inclusion removal capabilities of carbon based hexachloroethane as it releases dioxins, chlorine gas and corrodes foundry equipment. A secondary aim was to pioneer in-situ neutron diffraction to examine the solidification of magnesium alloys. The research involved preparing tensile samples of AZ91E magnesium alloy using permanent mould casting. Inclusion removal was conducted by using filtration and argon gas bubbling. Castings grain refined using hexachloroethane (0.25, 0.50 and 0.75 wt.%) were compared against ex-situ aluminum-silicon carbide and in-situ aluminum-carbon based grain refiners combined with filtration and argon gas bubbling. Further, in-situ neutron diffraction was utilized for phase analysis and fraction solid determination of magnesium-zinc and magnesium-aluminum alloys. There was a significant improvement in yield strength, ultimate tensile strength and elongation with filtration plus argon bubbling, carbon inoculation or both filtration plus argon bubbling and carbon inoculation. The results indicated that the mechanism of the observed ~20% reduction in grain sizes with carbon inoculation (hexachloroethane, ex-situ aluminum-silicon carbide and in-situ aluminum-carbon) was explained through duplex nucleation of Mn-Al and Al-Mg-C-O (likely Al2MgC2) phases. Finally, in-situ neutron diffraction was used to follow the formation of Mg17Al12 eutectic phase in a magnesium-9 wt.% aluminum alloy. For the magnesium-zinc alloys, in-situ neutron diffraction enabled characterization of the effects of zirconium to the fraction solid growth of (1010), (0002) and (1011) α-Mg planes. The societal and environmental impact of this research is significant. There is a clear demonstration of alternatives to the universally used hexachloroethane grain refiner promoting harmful emissions. Improved mechanical properties resulting from new grain refinement and iv inclusion filtration are a major advancement in promoting weight reduction, improved castability and decreased environmental impact for automotive and aerospace industries.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.020
GPT teacher head0.247
Teacher spread0.227 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicMagnesium Alloys: Properties and ApplicationsFrench-language works237,207