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Record W6990179417

Deformation characteristics and recrystallization behavior of an AZ31 magnesium alloy subjected to high speed rolling

2017· dissertation· en· W6990179417 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2017
Typedissertation
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsDeformation (meteorology)Recrystallization (geology)Dynamic recrystallizationMagnesium alloyAlloyMicrostructureFormability
DOInot available

Abstract

fetched live from OpenAlex

Magnesium AZ31 alloy sheets were rolled at 100 °C at three different speeds: (i) 1000 m/min (high speed rolling, HSR), (ii) 100 m/min (intermediate speed rolling, MSR) and (iii) 15 m/min (low speed rolling, LSR).High reductions of more than 70% were achieved in single pass by HSR and MSR, while the sheet fractured at a reduction of only 37% by LSR.During HSR and MSR, dynamic recrystallization (DRX) was observed at reductions of 37% and higher, which was the mechanism for the greater rollability; DRX is, in turn, related to the higher temperatures generated.Full recrystallization was achieved at reductions higher than 70% during MSR and HSR.The dominant DRX mechanism was twinning/shear banding induced DRX, which led to partially recrystallized and twinned microstructures at reductions from 37% to 58%.Thus, fine recrystallized grains formed along the shear bands, which possessed much higher number of twins and accommodated more dislocation slips.In regions outside shear bands, the twin density was much lower, continuous recrystallization can be seen in twin free regions as well as at twins.For a given reduction, the maximum intensity of the basal texture is weaker after HSR than after LSR, which is due to the activation of a larger number of contraction and secondary twinning and/or slip.The sheets were rolled at the high speed of 1000 m/min to six increasing reductions to generate a series of microstructures: (i) twinned and shear banded (8%-30%), (ii) partially DRXed and twinned (49%-58%) and (iii) fully DRXed (72%).These were annealed at temperatures from 200 °C to 500 °C for increasing time.As a comparison, the LSRed sheets were subjected to the same annealing conditions.Static recrystallization (SRX) kinetics were analyzed in terms of the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model and found to involve two sequential First and foremost, I would like to express my deepest gratitude to my supervisor, Professor Stephen Yue, for his guidance, patience and encouragement throughout my PhD project.Without his support, I would not have been able to pursue a great deal of my career endeavors and graduate experience.I would like to give special thanks to Prof. John J. Jonas for his helpful guidance and for imparting his profound knowledge of materials science to me.I am very grateful for his prompt response, perceptive advice and insightful discussion.I would like

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 score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.247
Teacher spread0.229 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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