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Effect of Hot Rolling and Equal-Channel Angular Pressing on Generation of Globular Microstructure in Semi-Solid Mg-3%Zn Alloy

2014· article· en· W2073555494 on OpenAlexaff
Łukasz Rogal, Frank Czerwiński, Lidia Lityńska‐Dobrzyńska, Piotr Bobrowski, Anna Wierzbicka-Miernik, J. Dutkiewicz

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsNatural Resources Canada
FundersNarodowe Centrum Badań i Rozwoju
KeywordsMaterials scienceMicrostructureAlloyEquiaxed crystalsSimaMetallurgyPressingDeformation (meteorology)Composite material

Abstract

fetched live from OpenAlex

A combination of hot rolling and equal channel angular pressing (ECAP) was explored to generate globular microstructures in the Mg-3%Zn alloy after re-heating to the semisolid state. It was found that the single-step deformation of as-cast alloy via hot rolling at 350°C with a thickness reduction of 50% refined the alloy microstructure by creating deformation bands of the Mg (α) phase with a size of the order of tenths of micrometers. After re-heating to 630 °C, the microstructure transformed into spheroidal morphologies with an average globule size of 82 μm. An additional deformation of the hot-rolled alloy by the ECAP method at 250 °C further refined the alloy microstructure to sub-micrometer grains of lath and equiaxed shapes. After re-heating of this microstructure to 630 °C the average globule size reached 62 μm, which is roughly 25% smaller than that achieved for the hot-rolled precursor. The role of strain-induced melt activation (SIMA) techniques in generation of globular morphologies in Mg-based alloys after partial re-melting is 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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.293
Teacher spread0.260 · 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 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
Published2014
Admission routes1
Has abstractyes

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Same venueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomenaSame topicMagnesium Alloys: Properties and ApplicationsFrench-language works237,207