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Record W2751449383 · doi:10.2351/1.5060318

Precision micro cutting of thin steel plate with newly designed laval nozzle by pulsed YAG laser

2004· article· en· W2751449383 on OpenAlexaboutno aff
Yasuhiro Okamoto, Yoshiyuki UNO, Masayoshi Hosogaya, Naoki Miyanagi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsDrossNozzleMaterials scienceJet (fluid)Mach numberMechanicsMechanical engineeringFlow (mathematics)MetallurgyEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper deals with laser cutting of thin steel plate by pulsed YAG laser using newly designed Laval nozzle. The assist gas flow spouted from the Laval nozzle dose not have Mach Shock Disk in free jet, and its straightness is superior to a traditional convergent nozzle. Therefore, the pressure on workpiece increases, and the dross height can be reduced remarkably even under low supply gas pressure condition. Thus, the consumption of flow quantity of assist gas can be reduced, when the dross height is equal. Besides, color change by excessive oxidation due to the heat conduction from dross is smaller at the back side of workpiece around the kerf, since Laval nozzle can remove melted material more effectively compared to a convergent nozzle. However, the supplied gas pressure should be carefully set, since Laval nozzle has many unstable regions, where the pressure on workpiece changes periodically. The dross height dose not increase or almost equal in the case of Laval nozzle, when the gap distance between the nozzle tip and the workpiece surface is changed from 1mm to 2mm. Because the assist gas flow from Laval nozzle has an excellent straightness and the pressure on workpiece does not reduce drastically. Moreover, it was made clear that Laval nozzle designed for the special condition can reduce the dross more effectively and decrease the 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)0.000
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 score0.016
Threshold uncertainty score0.530

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 head0.007
GPT teacher head0.207
Teacher spread0.200 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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