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Record W2050887597 · doi:10.2464/jilm.56.705

Effects of Mn content on bending formability of extruded AZ31 magnesium alloy pipes

2006· article· en· W2050887597 on OpenAlexaff
Jun-ichi SUGAI, Tadashi Iwai, Genjiro Motoyasu, Toru Shimizu, H. Soda, Alexander McLean

Bibliographic record

VenueJournal of Japan Institute of Light Metals · 2006
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFormabilityMaterials scienceMetallurgyManganeseDuctility (Earth science)Magnesium alloyAlloyUltimate tensile strengthBendingMicrostructureDeformation (meteorology)ElongationStrain (injury)Composite materialCompression (physics)Magnesium

Abstract

fetched live from OpenAlex

Formability of extruded round AZ31 magnesium alloy pipes with different manganese concentrations was evaluated by performing bending tests and measuring strain along the length of the pipes during the bending process. It was found that an increase in the amount of manganese content from 0.3% to 0.7% did not exert any significant effect on the values of yield stress, elongation, or compression to fracture, while the values of hardness tended to increase and UTS decreased. The compression ductility was significantly lower than tensile ductility for all manganese concentrations. Accordingly, the formability of pipes decreased, causing cracks in areas where compression strain occurred during bending. It was also found that a larger strain occurred in the latter half of the region along the length of pipe between the clamp-end and pipe positioning block. For alloy pipes with higher manganese concentrations (0.5 and 0.7 Mn mass%), there was a clear strain peak occurring in the strain profile in the region, while for the 0.3 Mn mass% alloy pipe it exhibited a more uniform strain profile along the length of the pipe in this region, indicating that a more uniform deformation took place which was confirmed by microstructure analysis of the specimens.

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)0.001
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 score0.008
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.234
Teacher spread0.212 · 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

Citations1
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Japan Institute of Light MetalsSame topicMagnesium Alloys: Properties and ApplicationsFrench-language works237,207