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Record W2897882494 · doi:10.2351/1.5061566

Effects of internal design geometry in de laval nozzles for off-axis assist gas injection on inert-gas laser cutting performance

2009· article· en· W2897882494 on OpenAlexaboutno aff
A. Riveiro, F. Quintero, R. Comesaña, M. Boutinguiza, F. Lusquiños, J. Pou

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLaser-induced spectroscopy and plasma
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleLaser cuttingSupersonic speedMechanical engineeringCoaxialAerodynamicsJet (fluid)Symmetry (geometry)LaserMaterials scienceMechanicsOpticsEngineeringAerospace engineeringPhysicsGeometryMathematics

Abstract

fetched live from OpenAlex

The aerodynamic interactions of the assist gas during fusion laser cutting are an essential parameter determining cutting performance. This subject has been object of multiple studies to determine the influence of the assist gas jet interactions with the workpiece in order to design assist gas injection systems which surpass the drawbacks exhibited by conventional cutting heads. Cutting heads incorporating off-axis de Laval nozzles to inject a supersonic gas jet has been demonstrated, by different authors, to be an effective option to increase performance and cut quality during fusion laser cutting of difficult materials. However, all efforts performed in this direction, have been accomplished using nozzles with axial symmetry. In this work, the fundamentals of a novel cutting head incorporating an off-axis nozzle, with non-axial internal symmetry are described. Gas flow inside the cut kerf is analyzed by means of flow visualization techniques. Moreover, cutting results are compared to those obtained through cutting heads assisted by axial-symmetric de Laval nozzles and sonic nozzles in a configuration off-axis and coaxial respectively with the laser beam.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.326
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.008
GPT teacher head0.221
Teacher spread0.214 · 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 teacher head, 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

Citations1
Published2009
Admission routes1
Has abstractyes

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