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Record W2571745140 · doi:10.20965/ijat.2010.p0510

Effect of Nozzle Shape on Micro-Cutting Performance of Thin Metal Sheet by Pulsed Nd: YAG Laser

2010· article· en· W2571745140 on OpenAlexaboutno aff
Yasuhiro Okamoto, Yoshiyuki UNO, Hiroshi Suzuki

Bibliographic record

VenueInternational Journal of Automation Technology · 2010
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDrossNozzleMaterials scienceCylinderLaser cuttingShock (circulatory)Composite materialMetallurgyLaserMechanical engineeringOpticsEngineering

Abstract

fetched live from OpenAlex

In precision laser cutting, the generation of dross can be reduced by using a high-pressure assist gas flow. However, our previous work made it clear that, for a conventional convergent nozzle, the pressure of assist gas on a workpiece is reduced to less than half of the cylinder gas pressure because of the generation of Mach Shock Disk. Furthermore, the removal of material from the micro-kerf becomes difficult since the fluidity of molten material is low in the narrow kerf. Therefore, nozzle shapes were investigated to improve the removal of material from the micro-kerf, and their effect on the fine cutting of a thin metal plate using a pulsed Nd: YAG laser was also discussed. The height of dross could be reduced by using a Laval throat nozzle with an initial expansion zone compared with that for a straight throat nozzle. The pressure on the workpiece was greater, and the straightness of the assist gas flow was excellent when the Laval throat nozzle with the initial expansion zone was used. Moreover, it was confirmed that the molten material could be effectively removed from the micro-kerf even at a middle cylinder gas pressure of around 600 kPa. Therefore, the Laval throat nozzle with the initial expansion zone can be used for fine cutting owing to its reduction of dross.

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 score0.026
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.002
GPT teacher head0.233
Teacher spread0.230 · 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

Citations5
Published2010
Admission routes1
Has abstractyes

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