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Record W3208822650 · doi:10.32920/ryerson.14653356.v1

Mechanical Properites Of Fiber Laser Welded And Friction Stir (Spot) Welded Lightweight Alloys

2021· preprint· en· W3208822650 on OpenAlexafffund
Mohammed S.H. Chowdhury

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsToronto Metropolitan University
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of WaterlooStrong
KeywordsMaterials scienceWeldingComposite materialUltimate tensile strengthHardening (computing)MetallurgyDuctility (Earth science)Layer (electronics)Creep

Abstract

fetched live from OpenAlex

Mechanical properties of fiber laser welded (FLWed), friction stir welded (FSWed), and friction stir spot welded (FSS weld) AZ31B-H24 Mg and Al 5754 alloys were studied. After welding, grains at the weld centre became recrystallized. β-Mg17A112 particles appeared in the fusion zone of the joints during laser welding, while a characteristic interfacial layer consisting of A112Mg17 and Al3Mg2 was observed in the A1/Mg dissimilar FSS weld. In FLWed joints, a joint efficiency of ~91% with superior yield strength, ultimate tensile strength and fatigue strength was achieved at a higher welding speed. In FSWed joints, a higher welding speed of 20 mm/s and lower rotational rate of 1000 rpm led to higher YS, but lower ductility, strain-hardening exponent and hardening capacity. In FSS weld joints, Mg/Mg, A1/A1 FSS welds and Al/Mg adhesive, Mg/A1 adhesive FSS welds had a significantly higher lap shear strength and fatigue life than the A1/Mg FSS weld.

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
Bibliometrics0.0010.000
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 head0.012
GPT teacher head0.234
Teacher spread0.222 · 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
Published2021
Admission routes2
Has abstractyes

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