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Record W3206413537 · doi:10.3390/ma14206069

Numerical Study on the Effect of Pre-Strain on Detwinning in Rolled Mg Alloy AZ31

2021· article· en· W3206413537 on OpenAlexaff
Chao Ma, Xing Wang Duan, Xiaoqian Guo, Lianying Zhang, Peidong Wu

Bibliographic record

VenueMaterials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceInflection pointCompression (physics)Tension (geology)Flow stressDeformation (meteorology)AlloyComposite materialMetallurgyGeometryMathematics

Abstract

fetched live from OpenAlex

The deformation behavior of rolled Mg alloy AZ31, previously compressed along the rolling direction (RD), was numerically investigated under reverse tension. The EVPSC-TDT model was employed to study the effect of pre-strain on detwinning for 3%, 6% and 9% pre-compressed materials along the RD. A new criterion was proposed to control the exhaustion of detwinning under reverse tension. Numerical results show good agreement with the corresponding experimental data. It was demonstrated that the proposed criteria can capture the key features associated with detwinning in pre-compressed materials. Regardless of the amount of pre-compression, detwinning is activated under reverse tension, leading to low yield stress and a typical s-shaped flow curve. The inflection point reflects the exhaustion of detwinning, which is delayed when increasing the amount of pre-compression.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.019
GPT teacher head0.271
Teacher spread0.252 · 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 designSimulation or modeling
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

Citations6
Published2021
Admission routes1
Has abstractyes

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