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Record W2526296184 · doi:10.1080/02670836.2016.1216263

Microstructural and mechanical features of aluminium semi-solid alloys made by rheocasting technique

2016· article· en· W2526296184 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

VenueMaterials Science and Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsMaterials scienceMicrostructureAluminiumSlurryMetallurgyAlloyScanning electron microscopeUltimate tensile strengthAluminium alloyOptical microscopeComposite material

Abstract

fetched live from OpenAlex

The rheocasting process applied by Swirled Enthalpy Equilibration Device (SEED) technique relies on rapid extraction of a controlled quantity of heat from the liquid aluminium alloy via mechanical agitation to form the semi-solid slurry that can be formed under pressure. Microstructural characteristics of both conventional and semi-solid A357 castings under T6 heat treatment conditions were examined using optical and scanning electron microscopy. The fatigue and tensile experiments were applied to evaluate the effect of SEED technique on the mechanical properties of T6-A357 semi-solid alloys and conventional castings. The results showed that the rheocasting–SEED technique has proved successful in producing optimum microstructure of Al–Si–Mg semi-solid alloys providing an excellent combination of quality and mechanical performance as compared to conventional technique. This paper is part of a Themed Issue on Aluminium-based materials: processing, microstructure, properties, and recycling.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.599

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 studies0.0000.002
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 head0.005
GPT teacher head0.204
Teacher spread0.199 · 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