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Mathematical Modeling and Microstructure Analysis of Al–Mg–Sc–Zr Alloy Strips Produced by Horizontal Single Belt Casting (HSBC)

2014· article· en· W2087407836 on OpenAlex
Sa Ge, Mert Çelikin, Mihaiela Isac, R. I. L. Guthrie

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueISIJ International · 2014
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceCastingAlloyMicrostructureMetallurgyContinuous castingComposite material

Abstract

fetched live from OpenAlex

Horizontal Single Belt Casting (HSBC) is a near net shape strip casting technology that will probably gain significant prominence in the coming years. Fluid mechanics and associated heat and mass transfer are important aspects of any continuous casting process, and the HSBC process is no exception.In this study, mathematical models have been developed, using ANSYS FLUENT 14, to assess various aspects of the HSBC process for the Al–Mg–Sc–Zr system. Specific emphasis is placed on a) the effects of substrate surface properties on strip quality, b) liquid metal-air two-phase interactions and meniscus behavior, c) heat fluxes between the metal and substrate, and d) solidification behavior during strip casting.These predictions are validated against experimental casting results. A 5000 series Al–Mg alloy, with added Sc and Zr, shows exceptional potential as a structural material for aerospace and transportation applications. It is also a suitable material to be produced via the HSBC process. Optical microscopy, SEM and EBSD analyses were conducted to compare the potential advantages of casting this alloy via the HSBC process versus conventionally produced Direct Chill Casting.

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.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.884
Threshold uncertainty score0.855

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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