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Record W2886431641 · doi:10.1007/s11663-018-1372-y

Transient Rheological Behavior of Semisolid SEED-Processed 7075 Aluminum Alloys in Rapid Compression

2018· article· en· W2886431641 on OpenAlexafffund
Amir Bolouri, X.-Grant Chen

Bibliographic record

VenueMetallurgical and Materials Transactions B · 2018
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of the West of England
KeywordsMaterials scienceRheologyGrain sizeShear rateApparent viscosityAlloySlurryViscosityMicrostructureComposite materialShear (geology)MetallurgyAluminium

Abstract

fetched live from OpenAlex

Abstract The transient rheological behavior and microstructure evolution of semisolid SEED-processed 7075 aluminum alloys were studied using the rapid compression tests. The effects of the TiB 2 grain refinement on the grain morphology and size of semisolid slurries were investigated. Results indicated that the grain refiner could reduce the grain size and improve the globularity of α-Al grains. The grain-refined alloy can be easily deformed at a wide range of solid contents (0.42 to 0.53 Fs), in which the deformation level appears to be independent from the solid content. Under the transient state, the apparent viscosity decreased with increasing shear rate to a minimum value and followed by an increase as the shear rate decreased. The apparent viscosity of the base alloy exhibited a dependency on the solid content, while the apparent viscosity of the grain-refined alloy in the decreasing or increasing shear rate periods was not substantially influenced by the solid content. The viscosity as a function of applied shear rate can be described using the power law viscosity model. The differences in the flow behavior index ( n ) and the consistency index ( k ) for two alloys were discussed.

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 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 categoriesInsufficient payload (model declined to judge)
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 score0.025
Threshold uncertainty score0.999

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)0.0020.000

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 head0.012
GPT teacher head0.213
Teacher spread0.201 · 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 teacher head, not a consensus.

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

Citations8
Published2018
Admission routes2
Has abstractyes

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Same venueMetallurgical and Materials Transactions BSame topicAluminum Alloy Microstructure PropertiesFrench-language works237,207