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Record W2318820379 · doi:10.2493/jspe.70.433

Effects of Laval Nozzle on Precision Micro Cutting of Thin Metal Plate by Pulsed YAG Laser

2004· article· en· W2318820379 on OpenAlexaboutno aff
Yasuhiro Okamoto, Yoshiyuki UNO, Yoshihumi Murakami, Masayoshi Hosogaya, Naoki Miyanagi

Bibliographic record

VenueSeimitsu kougakkaishi rombunshuu/Seimitsu kougakkaishi/Seimitsu Kougakkaishi rombunshuu · 2004
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsNozzleDrossMechanical engineeringMechanicsMaterials scienceCylinderMach numberFlow (mathematics)Laser cuttingMachiningLaserEngineeringMetallurgyOpticsPhysics

Abstract

fetched live from OpenAlex

The stagnation pressure, which is the sum of static and dynamic pressures, has a great influence on machining performance in laser cutting. Therefore, increasing the velocity of assist gas is effective in reducing the height of dross. In this paper, Laval nozzle was newly designed, because it can increase the velocity of assist gas flow spouted from nozzle tip. Effects of Laval nozzle on assist gas flow and the machining results in precision cutting of thin metal plate by a pulsed YAG laser were experimentally investigated. Assist gas flow from Laval nozzle does not have Mach Shock Disk and goes more straight than a traditional convergent nozzle. Therefore, the pressure on a workpiece increases, since assist gas flow from Laval nozzle can utilize energy more efficiently. However, caution is necessary in setting the cylinder gas pressure, since Laval nozzle has many unstable regions, where the pressure on the workpiece changes periodically. Using Laval nozzle makes it possible to reduce the height of dross even under low cylinder gas pressure condition compared to convergent nozzle. Thus the consumption of flow quantity of assist gas can be reduced, when the same height of dross is desired. Moreover, it was pointed out that the convexed inner wall of nozzle leads to the reduction of dross generation and unstable region.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0040.004
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0020.005
Science and technology studies0.0010.001
Scholarly communication0.0010.004
Open science0.0040.001
Research integrity0.0030.004
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 head0.005
GPT teacher head0.211
Teacher spread0.206 · 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; both teacher heads agree on what is shown here.

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
Published2004
Admission routes1
Has abstractyes

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