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Record W2065152812 · doi:10.2351/1.1809633

Comprehensive assessment of the CO2 laser cut quality of ceramics with different assist gas injection systems

2004· article· en· W2065152812 on OpenAlexaboutno aff
F. Quintero, J. Pou, F. Lusquiños, M. Boutinguiza, R. Soto, M. Pérez‐Amor, Florian Wagner

Bibliographic record

VenueJournal of Laser Applications · 2004
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsnot available
FundersEuropean Commission
KeywordsMaterials scienceNozzleCeramicLaser cuttingLaser beam machiningMachiningSurface roughnessMechanical engineeringMulliteLaserProcess (computing)Surface finishWork (physics)Quality (philosophy)Composite materialProcess engineeringEngineering drawingMetallurgyComputer scienceOpticsLaser beamsEngineering

Abstract

fetched live from OpenAlex

C O 2 laser cutting is an efficient and advantageous process for cutting of ceramics when the hardness of such materials makes the conventional machining methods unproductive. At the same time, the application of laser cutting to ceramics involves the assessment of the different process parameters to select the suitable conditions for every specific ceramic. In this work, a comprehensive analysis of the CO2 laser cutting of mullite-alumina is presented. The cut quality was assessed under the criterion of facilitate the comparison of the results obtained using different process parameters and two different assist gas injection systems. For this reason, some quantitative standard parameters were analyzed (kerf width, roughness, perpendicularity), besides of the preliminary survey of some features and the microscopic examination of the heat affected zone. The results demonstrate the improvement of the cut quality using an assist gas injection system based on an off-axis De Laval nozzle.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.019
GPT teacher head0.288
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2004
Admission routes1
Has abstractyes

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Same venueJournal of Laser ApplicationsSame topicLaser Material Processing TechniquesFrench-language works237,207