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Record W2998093254 · doi:10.3390/ma13010249

Reducing Yield Asymmetry between Tension and Compression by Fabricating ZK60/WE43 Bimetal Composites

2020· article· en· W2998093254 on OpenAlexaff
Kangning Zhao, Dexing Xu, Xiao Song, Yingzhong Ma, Hongxiang Li, Jishan Zhang, D.L. Chen

Bibliographic record

VenueMaterials · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsToronto Metropolitan University
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceCrystal twinningComposite materialSlip (aerodynamics)BimetalTension (geology)RodComposite numberDeformation mechanismAsymmetryCompression (physics)MicrostructureMetallurgy

Abstract

fetched live from OpenAlex

In this study ZK60/WE43 bimetal composite rods were manufactured by a special method of hot diffusion and co-extrusion. Interface microstructure, deformation mechanism, and yield asymmetry between tension and compression for the composite rods were systematically investigated. It was observed that the salient deformation mechanism of the ZK60 constituent was {10-12}<-1011> extension twinning in compression and prismatic slip in tension, and different deformation modes resulted in yield asymmetry between tension and compression. In contrast, the WE43 constituent tends to be more isotropic due to grain refinement, texture weakening, solid-solution and precipitation strengthening, which were deformed via basal slip, prismatic slip, and {10-12}<-1011> extension twinning in both tension and compression. Surprisingly, it was found that yield asymmetry between tension and compression for the ZK60/WE43 composite rods along the extrusion direction was effectively reduced with a compression-to-tension ratio of ~0.9. The strongly bonded interface acting as a stress transfer medium for the ZK60 sleeve and WE43 core exhibited the coordinated deformation behavior. This finding provides an effective method to decrease the yield asymmetry between tension and compression in the extruded magnesium alloys.

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 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 score0.002
Threshold uncertainty score0.824

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)0.0010.000

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 head0.034
GPT teacher head0.240
Teacher spread0.206 · 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
Published2020
Admission routes1
Has abstractyes

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