MétaCan
Menu
Back to cohort
Record W4250405502 · doi:10.32920/ryerson.14649522

Novel grain refinement of AZ91E magnesium alloy and the effect on hot tearing during solidfication

2021· preprint· en· W4250405502 on OpenAlexaff
Abdallah Elsayed

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceCastabilityTearingEutectic systemMetallurgyGrain sizeAlloyMagnesium alloyCastingMagnesiumDislocationComposite material

Abstract

fetched live from OpenAlex

For the A1-5Ti-1B grain refiner, the addition of 0.1 wt.% provided a 68 % reduction in grain size as compared to the unrefined AZ91E alloy at a holding time of five minutes. Grain growth restriction by TiB₂ particles was the source of grain refinement. With the addition of A1-5Ti-1B, only a small reduction in hot tearing susceptibility ws observed because large TiA1₃ particles bonded poorly with the eutectic and blocked feeding channels.The addition of 1.0 wt.% A1-1Ti-3B provided a grain size reduction of 63% as compared to the unrefined AZ91E alloy at a holding time of five minutes. The grain refinement with A1-1Ti-3B addition was attributed to a combination of TiB₂ grain growth restriction and A1B₂ nucleating sites. A significant reduction in hot tearing susceptibility was observed with A1-1Ti-3B addition as a result of a higher cooling rate and shorter local soldification time as compared to the AZ91E alloy. The reduction in hot tearing susceptibility was attributed to the good interface between eutectic and TiB₂ particles. Both grain refiners demonstrated a good resistance to fading during the holding times investigated. In addition, the AZ91E + A1-5Ti-1B and AZ91E + A1-1Ti-3B castings showed much fewer dislocation networks as compared to the untreated AZ91E casting.The development of efficient A1-Ti-B refiners can also improve castability of magnesium alloys. In addition, the fade resistant A1-Ti-B grain refiners can reduce operating costs and maintain productivity on the foundry floor. Thus, magnesium alloy with A1-Ti-B treatment have the potential for more demanding structural applications in the automobile and aerospace industries. Vehicle weight in the aerospace and automotive industries directly impacts carbon emissions and fuel efficiency. An increase in the use of lightweight materials for structural applications will result in lighter vehicles. Low density materials, such as magnesium (1.74 g/cm³) are a potential alternative to aluminium (2.70 g/cm³), to reduce component weight in structural applications.However, current magnesium alloys still do not have adequate mechanical properties and castability to meet the performance specifications of the automotive and aerospace industries. Grain refinement can significantly improve mechanical properties and reduce hot tearing during permanent mould casting. Recently, Al-Ti-B based grain refiners have shown potential in grain refining magnesium-aluminum alloys such as AZ91E. This study investigates the grain refining efficiency and fading of A1-5Ti-1B and A1-1Ti-3B in AZ91E magnesium alloy and their subsequent effect on hot tearing.The grain refiners were added at 0.1, 0.2, 0.5 and 1.0 wt.% levels. For the grain refinement and fading experiments, the castings were prepared using graphite moulds with holding times of 5, 10 and 20 minutes. For the hot tearing experiments, castings were produced representing the optimal addition level of each grain refiner. The castings were prepared using a permanent mould with pouring and mould temperatures of 720 and 180 ºC, respectively. The castings were characterized using SEM, TEM, optical microscopy and thermal analysis.

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 score0.003
Threshold uncertainty score0.006

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 head0.015
GPT teacher head0.235
Teacher spread0.220 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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