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Record W2369642304

Research, Development and Application of High-effiency Ultrasonic PlazJet Coating Technique

2003· article· en· W2369642304 on OpenAlexaboutno aff
Binshi Xu

Bibliographic record

VenueManufacturing Technology & Machine Tool · 2003
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsCoatingMaterials scienceUltrasonic sensorJet (fluid)Composite materialCeramicParticle (ecology)Gas dynamic cold sprayMechanical engineeringAcousticsMechanicsPhysicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

According to the design idea of the high_efficiency and ultrasonic plazjet gun, structures of the gun body, water path, air path, the negative and positive poles and the powder delivering structure have been newly designed, and a unique single_negative pole Laval jet has been designed using the plasma physics, fluid mechanics and engineering thermodynamics theories. In the coating specifications detecting of the plazjet gun and the coating, the high_efficiency and ultrasonic spray coating have been realized under low power (80kW) and low air fluid (6m 3/h), and the particle flying speed reaches 450 m/s within the effective spray coating distance. The main specifications of the jet gun such as the flame moving speed, the powder sedimentation efficiency, ratio of the coating energy consumption and the cost efficiency of the pole have reached or surpassed those of the PlazJet high efficiency ultrasonic coating gun of TAFA Company of America. The bond strength, pore space and microhardness of the ceramics coating prepared by this gun are better obviously than the common METCO.9M plasma gun, but the running cost is only the half of the foreign ultrasonic PlazJet coating.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.008
GPT teacher head0.242
Teacher spread0.233 · 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

Citations0
Published2003
Admission routes1
Has abstractyes

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