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Record W2019318422 · doi:10.1081/amp-200060436

Continuous Wave ND:YAG Laser Welding of Sand-Cast ZE41A-T5 Magnesium Alloys

2005· article· en· W2019318422 on OpenAlexaff
X. Cao, Min Xiao, Mohammad Jahazi, J.-P. Immarigeon

Bibliographic record

VenueMaterials and Manufacturing Processes · 2005
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceMagnesiumMetallurgyWeldingLaserLaser beam weldingMagnesium alloyCast ironComposite materialOptics

Abstract

fetched live from OpenAlex

A continuous wave 4 kW Nd:YAG laser system was used to weld 2-mm butt joints of sand-cast ZE41A-T5 magnesium alloys at a power of 2.5 kW, welding speed of 6.0 m/min, and defocusing distance from − 2 to + 3 mm for the material in the machined surface conditions. It was found that the adjustment of defocusing distance greatly influences the establishment of conduction or keyhole mode welding. Conduction welding is obtained at a power density of 4.0 × 10⁵ W/cm². Keyhole welding is reached at a threshold irradiance of 1.5 × 10⁶ W/cm². The fusion zone consists of refined equiaxed grains formed through cellular growth in the Zr-containing magnesium alloys. The partially melted zone is rather narrow, only a few grains wide. No grain growth or coarsening but softening is observed in the heat affected zone (HAZ). The weld defects observed include three main types: imperfect shape, cavities, and weld cracks. The mechanisms of their formations are discussed. In addition, the original cast quality was found to have a significant influence on the formation of defects such as underfill, surface depression, porosity, and burn-through during laser welding.

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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.015
GPT teacher head0.219
Teacher spread0.203 · 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

Citations56
Published2005
Admission routes1
Has abstractyes

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