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Record W4405058937 · doi:10.1177/13621718241290543

Effects of the laser spot size and beam wobbling on the properties of aluminum 6061 weld metal

2024· article· en· W4405058937 on OpenAlexafffund
Kazuki Matsuda, Nivas Ramachandiran, Hanwen Yang, A.P. Gerlich

Bibliographic record

VenueScience and Technology of Welding & Joining · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced Welding Techniques Analysis
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceEquiaxed crystalsSpot weldingWeldingLaser beam weldingMicrostructureAluminiumLaserGrain sizeMetallurgyBeam (structure)Weld poolAlloyHeat-affected zoneComposite materialOpticsGas tungsten arc weldingArc welding

Abstract

fetched live from OpenAlex

The effects of the spot size and beam wobbling on the mechanical properties and microstructure of the AA 6061 aluminum alloy during laser welding have been investigated. Changing the spot size from 0.6 to 0.3 mm reduced the required heat input for full penetration by 56% for a static laser beam and by 62% for a wobbling laser beam. Using a 0.3 mm diameter spot with beam wobbling reduced the severity of surface defects such as undercuts and excessive reinforcement. When the heat input and bead width were increased, laser beam wobbling increased the width of the equiaxed microstructure region in the weld metal. Grain refinement and the reduction in solute strengthening due to evaporation only had small effects on the weld metal strength.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.007
GPT teacher head0.207
Teacher spread0.199 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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