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Record W2896821535 · doi:10.2351/1.5060832

Multifunctional hand-held laser processing device

2006· article· en· W2896821535 on OpenAlexaboutno aff
Christian Hennigs, Oliver Meier, Andreas Ostendorf, H. Haferkamp

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsLaser cuttingLaserNozzleModular designMechanical engineeringDeflection (physics)Laser beam weldingLaser beam machiningComputer scienceOpticsLens (geology)Materials scienceWeldingEngineeringLaser beamsPhysics

Abstract

fetched live from OpenAlex

For the economical dismantling of nuclear installations, modular cutting systems are necessary to enable a low-emission cutting process particularly for high sheet thicknesses. Hand-held laser processing devices offer this possibility with a flexible implementation. In this paper, a newly developed hand-held laser processing device will be presented, which applies a novel bifocal optic, a special cutting system with two-gas Laval nozzles, and makes a multifunctional application area available. Laser notching, cutting, material removal, and prenotching in combination with laser cutting are already possible with this system. Extended modules for surface treatments and welding applications are under development. Besides its many fields of application, the processing device HLG 3000-sk-df offers additionally wide adaptation possibilities in a very light (3.5 kg) and compact design. This can be realized with the special mirror geometry, which combines a beam deflection mirror and a focusing lens. For the first time, the novel bifocal optic enables laser prenotching and cutting in one cycle using a hand-held laser processing device. The double focus configuration of the laser beam results from the splitted mirror design. The cutting system based on two-gas Laval nozzles constitutes a high potential for laser cutting applications resulting in high cutting qualities also for high sheet thicknesses and avoiding burr adhesion.

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 score0.324
Threshold uncertainty score0.414

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 head0.009
GPT teacher head0.211
Teacher spread0.202 · 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

Citations3
Published2006
Admission routes1
Has abstractyes

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