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Record W7132281665

Feedstock powder considerations for miniaturized inner-diameter HOVF systems for the deposition of WC-Co-Cr coatings

2023· other· en· W7132281665 on OpenAlexvenueno aff
Maniya Aghasibeig

Bibliographic record

VenueNPARC · 2023
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsThermal sprayingAbrasion (mechanical)Raw materialDeposition (geology)Particle sizeParticle (ecology)AerospaceParticle-size distributionCarbide
DOInot available

Abstract

fetched live from OpenAlex

Emerging miniature HVOF systems offer new opportunities to replace hard-chrome plating with sprayed carbide coatings in space restricted inner-diameters in aerospace and industrial application. The worldwide interest in this technology is largely driven by regularity restrictions for the continuation of downstream use of hexavalent chrome, such as REACH. While feedstock supply chains for conventional HVOF WC-Co-Cr are well established since decades, the lower flame enthalpies and shorter spray distances used for the scaled-down HVOF systems impose smaller particle size cuts as customized to the particular spray system to attain sufficient particle heating and acceleration. This presentation discusses the feedstock particle size and size distribution considerations for the liquid fueled Praxair TAFA Model 825 JPid, the hydrogen fueled Spraywerx ID-NOVA MK-6 HVOF, and the Uniquecoat i7 HVAF systems for the deposition of wear protective WC-Co-Cr coatings. A variety of custom manufactured and commercially available powders are compared and suitable windows for particle size distributions, torch input powers and spray distances are delineated for the deposition of coatings exceeding harness values of 950 HV300gf, porosities below 1%, and ASTM 65 abrasion wear loss below 0.1g per 6000 revolutions.

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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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 head0.038
GPT teacher head0.294
Teacher spread0.256 · 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
Published2023
Admission routes1
Has abstractyes

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