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Record W4301221901 · doi:10.31399/asm.cp.itsc2015p0076

HVOF and HVAF Coatings of Nano-Agglomerated Tungsten Carbide–Cobalt Powders for Water Droplet Erosion Application

2015· article· en· W4301221901 on OpenAlexaff
Fariba Tarasi, M.S. Mahdipoor, Ali Dolatabadi, Mamoun Medraj, Christian Moreau

Bibliographic record

VenueThermal spray · 2015
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsConcordia University
Fundersnot available
KeywordsCermetThermal sprayingMaterials scienceTungsten carbideMetallurgyCarbideTungstenCeramicCoatingComposite material

Abstract

fetched live from OpenAlex

Abstract Water droplet erosion (WDE) is a well-known phenomenon. This type of erosion is due to the impingement of water droplets of several hundred microns to a few millimeters size at velocities of hundreds of meters per second on the edges and surfaces of components. The solution to this problem is in high demand especially for the moving blades of gas turbines’ compressors and those operating at the low-pressure (LP) end of steam turbines. Thermal sprayed tungsten carbide based coatings have been the focus of many studies and are industrially accepted for a multitude of wear and erosion resistance applications. The present work studies the microstructural, phase analysis and mechanical properties and their effects on water droplet erosion resistance of such coatings deposited with high velocity oxygen fuel (HVOF) and high velocity air fuel (HVAF) processes. The feed nano-agglomerated tungsten carbide-cobalt powders are in either sintered or non-sintered conditions. The WDE tests were performed using 0.4 mm water droplets at 300 m/s impact velocity. The study shows promising results for this cermet (better than the Ti6Al4V bulk material) as WDE resistant coatings when deposited using HVOF or HVAF processes under optimum conditions.

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 score0.001
Threshold uncertainty score0.564

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 head0.013
GPT teacher head0.226
Teacher spread0.213 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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