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Record W2945848214 · doi:10.2351/1.5061272

Laser beam welding of titanium – A comparison of CO2 and fiber laser for potential aerospace applications

2008· article· en· W2945848214 on OpenAlexaff
Steffen Mueller, Craig Bratt, Peter P. Mueller, J. Cuddy, Kartik Shankar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWelding Techniques and Residual Stresses
Canadian institutionsResearch Manitoba
Fundersnot available
KeywordsMaterials scienceWeldingLaser beam weldingLaserFiber laserYtterbiumTitanium alloyTitaniumLaser power scalingMetallurgyHeat-affected zoneAerospaceLaser beam qualityComposite materialAlloyOptoelectronicsOpticsLaser beamsFiberDopingEngineering

Abstract

fetched live from OpenAlex

The Laser welding process is increasingly being considered for joining titanium alloy airplane structures and also for manufacture, repair and overhaul of titanium aeroengine components. Recent advancement in gas and solid state laser technology has resulted in the availability of higher beam quality Lasers which can produce narrow welds with low heat input and high weld speeds. This paper describes a study of Laser welding of the Titanium alloys Ti6-4 and Ti5553 using two types of high power Laser - a CO2 Slab Laser and a Ytterbium-Fiber Laser. The differences between both Lasers and their effect on weld quality and performance will be explained. The weld results have been evaluated referring to AWS weld quality standards, with particular reference to porosity appearance and weld profile. In addition, micro hardness measurements and tensile test data will be shown.

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 score0.006
Threshold uncertainty score0.012

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.015
GPT teacher head0.253
Teacher spread0.238 · 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

Citations10
Published2008
Admission routes1
Has abstractyes

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