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Record W4387743073 · doi:10.5772/intechopen.112987

A Review on Fundamentals of Grain Refining of Al-Si Cast Alloys

2023· review· en· W4387743073 on OpenAlexaff
E. Isaac Samuel, Hicham Tahiri, A. M. Samuel, Victor Songméné, F. H. Samuel

Bibliographic record

VenueIntechOpen eBooks · 2023
Typereview
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsRefining (metallurgy)Materials scienceMachinabilitySuperheatingEquiaxed crystalsMetallurgyAlloyCastingAluminiumMolten metalThermodynamics

Abstract

fetched live from OpenAlex

Grain refining is considered one of the most important liquid metal processing processes for aluminum alloys. Three different types of grain morphology are possible: columnar, twin columnar and equiaxed. The present work reviews most of the theories that were proposed during the past three decades. These theories were mainly based on thermal analysis and thermodynamics to explain the mechanisms of grain refining of Al-Si based alloys, including the role of the master alloy used i.e., Al-B, Al-Ti, and Al-Ti-B alloys. Other aspects were also examined, mainly the interactions between Si and/or Sr and the grain refining master alloy, superheating of the molten metal as well as holding time prior to casting. This phenomenon is normally termed “poisoning” since it reduces the effectiveness of the added grain refiners. The effects of grain refining on the alloy microstructural characteristics, mechanical properties, machinability, hot tearing etc. have not been addressed in the present article.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.005

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 head0.084
GPT teacher head0.333
Teacher spread0.249 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations7
Published2023
Admission routes1
Has abstractyes

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