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Record W4402509510 · doi:10.1016/j.jmrt.2024.09.098

Microstructure and mechanical properties of a low-alloyed Mg–Zn–Al–Ca alloy: Effect of extrusion speed

2024· article· en· W4402509510 on OpenAlexafffund
Lei Liu, Hua Wang, Datong Zhang, Cheng Qiu, D.L. Chen

Bibliographic record

VenueJournal of Materials Research and Technology · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Toronto
FundersSpecial Project for Research and Development in Key areas of Guangdong ProvinceNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMicrostructureExtrusionAlloyMetallurgy

Abstract

fetched live from OpenAlex

A low-alloyed Mg-1.2Zn-0.6Al-0.1Ca (wt.%) alloy was extruded at 200 °C with different ram speeds (0.5–4.0 mm/s), and the microstructure and mechanical properties were studied systematically. Heterostructures with fine dynamic recrystallized (DRXed) grains and coarse unDRXed grains were achieved at lower ram speeds of 0.5 mm/s and 1.0 mm/s, and fully-DRXed microstructure was attained at 4.0 mm/s. Increasing the extrusion speed resulted in an increase in DRXed grain size from 0.9 μm to 3.8 μm, and a transformation of the DRXed texture component from <10 to 10>−<11–20> to a new orientation that deviated by approximately 14°. The sample extruded at 0.5 mm/s presented an excellent tensile yield strength (TYS) of 369 MPa along with a 7.8% elongation, which was mainly due to the high hetero-deformation induced (HDI) strengthening provided by its heterostructures. Increasing ram speed resulted in an improved elongation despite a decreased TYS. The reasons for the decreased strength with increasing extrusion speed were mainly associated with grain growth, reduced dislocation density and weakened HDI strengthening. The reasons for the improved ductility with increasing extrusion speed were largely due to the increased DRXed grains fraction with soft orientations.

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)0.002
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 score0.005
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.022
GPT teacher head0.302
Teacher spread0.280 · 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.

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

Citations9
Published2024
Admission routes2
Has abstractyes

Explore more

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