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Reduction of Hot Tearing of Cast Semi-Solid 206 Alloys

2012· article· en· W2006914137 on OpenAlex

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

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2012
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsTearingCastabilityMaterials scienceSiliconAlloyAluminiumCastingComposite materialMetallurgy

Abstract

fetched live from OpenAlex

The mechanical properties of 206 alloys are among the highest of aluminum alloys. However, these alloys are usually prone to hot tearing. It is known that the addition of silicon can reduce the hot tearing propensity and improve fluidity. However, the commercial 206 alloys used in conventional casting processes limit the silicon concentration ≤0.05 wt% to obtain good mechanical properties. However, the semi-solid forming offers a unique opportunity to increase the silicon content to improve the castability without compromise on mechanical properties. In the present paper, the development of modified 206 alloy compositions to minimize hot tearing during semi-solid forming while maintaining competitive mechanical properties is reported. The effect of high silicon contents with varying copper levels on hot tearing sensitivity is studied. The mechanical properties of a high Si 206 alloy with lowest hot tearing sensitivity are evaluated. It is found that increasing the silicon content in 206 alloys is beneficial to reduce hot tearing. The high Si 206 variants produced by the SEED rheocating process not only reduce significantly the hot tearing sensitivity but also attain superior mechanical properties.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.004
Open science0.0030.004
Research integrity0.0000.001
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 head0.033
GPT teacher head0.271
Teacher spread0.238 · 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