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Record W4250446997 · doi:10.32920/ryerson.14648367

Mechanical Properties Of Butt Welded AZ31B Magnesium Alloys

2021· preprint· en· W4250446997 on OpenAlexaff
Md. S.M. Chowdhury

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMaterials scienceWeldingUltimate tensile strengthMetallurgyComposite materialFatigue limitLaser beam weldingButt jointAlloyBrittlenessJoint (building)Structural engineering

Abstract

fetched live from OpenAlex

Mechanical properties of friction stir welded (FSWed), double sided arc welded (DSAWed), fiber laser welded (FLWed) and diode laser welded (DLWed) on AZ31B Mg alloy were studied. After welding, grains at the centre became recrystallized. Brittle phase β-Mg₁₇AI₁₂ particles observed at the centre of the joint during fusion welding process. The yield strength (YS), ultimate tensile strength (UTS) and fatigue strength were lower in the FDWed samples than in the DSAWed samples. Welding defect at the bottom of the FDWed joint was observed when right hand thread (RHT) weld tool was considered. In FLWed joint, YS, UTS and fatigue strength, with a joint efficiency of ~91% was achieved while the YS, UTS and fatigue strength of the DLWed joints were notably lower. The DSAWed joints and DLWed joints exhibited a higher strain hardening capacity in comparison with the FSWed joints and FLWed joints, respectively.

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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.227
Teacher spread0.207 · 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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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