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Record W2972917061 · doi:10.2351/1.5061251

Principles of supersonic oxygen jet forming for Lasox cutting process

2008· article· en· W2972917061 on OpenAlexaboutno aff
G. V. Ermolaev, О. Б. Ковалев, А. Г. Маликов, А. М. Оришич, V. B. Shulyatyev, А. В. Зайцев

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicErosion and Abrasive Machining
Canadian institutionsnot available
Fundersnot available
KeywordsSupersonic speedJet (fluid)Process (computing)Mechanical engineeringMaterials scienceComputer scienceForming processesAerospace engineeringEngineering drawingEngineering

Abstract

fetched live from OpenAlex

Lasox, a hybrid laser-assisted oxygen-cutting technology, was developed not long time ago. The process is realized by the super sonic oxygen jet accomplished with laser beam with rather moderate power about 2kW. The major role is played by oxygen cutting jet. In certain, well known, minimum laser preheatening conditions stable heterogeneous iron-oxygen combustion reaction takes place on cutting front. One of the major problems is optimisation supersonic cutting head with de’Laval nozzle witch produces effective supersonic oxygen jet. In this work simple principle of supersonic nozzle parameters selection for Lasox cutting process is presented. Postulated, that for good quality cuts the jet is to be over expanded or designed: smooth with a minimum of shock disks and other peculiarities. Flow inside cut kerf is to be supersonic, with good impact. The motion of the gas inside nozzle is considered as an isentropic process. These conditions are enough to construct nozzle geometry able to cut required plate thickness with required kerf width. In such a way method of supersonic nozzle geometry selection for thick section cutting Lasox is proposed. Known experimental points are in good agreement with theoretical prediction.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.611
Threshold uncertainty score0.783

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)0.0010.000

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 head0.043
GPT teacher head0.273
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 designObservational
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
Published2008
Admission routes1
Has abstractyes

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