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Record W2806227924 · doi:10.1155/2018/7626219

Effect of Melt Temperature on the Effectiveness of the Grain Refining in Al‐Si Castings

2018· article· en· W2806227924 on OpenAlexaff
A. M. Samuel, S. S. Mohamed, H. W. Doty, S. Valtierra, F. H. Samuel

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsCégep de ChicoutimiUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMaterials scienceRefining (metallurgy)Metallurgy

Abstract

fetched live from OpenAlex

The results inferred from the present work show that Al3Ti phase has a strong affinity to react with silicon (Si) in the molten alloy leading to formation of (Al,Si)3Ti phase instead. This reaction is independent of the grain refiner type. The molten liquid temperature would change its morphology from platelets at 750°C into dendritic structure at 950°C. It has also been observed that (Al,Si)3Ti phase platelets precipitate within the α‐aluminum dendrites, whereas TiB2 or AlB2 particles are released into the surrounding interdendritic regions. Introduction of the grain refiner, regardless its type, would cause change in the α‐aluminum dendrite morphology from an elongated to a more rounded form. The results also reveal that addition of 100 ppm B will reduce the initial grain size by ∼85% which is more than the effect of addition of 0.2%Ti in the form of Al‐10%Ti (about 65%). Elimination of undercooling is important to obtain the maximum grain refining effect.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.003
GPT teacher head0.211
Teacher spread0.208 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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