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Record W4402154544 · doi:10.31399/asm.cp.itsc2024p0643

Deposition and Characterization of Thermally Sprayed Metallic Coatings onto Polymer Reinforced Carbon Fiber Composites

2024· article· en· W4402154544 on OpenAlexaff
Dilkaram S. Ghuman, Marie-Laurence Cliche, Bruno C. N. M. de Castilho, Fadhel Ben Ettouil, Christian Moreau, Chunzou Pan, Pantcho Stoyanov

Bibliographic record

VenueThermal spray · 2024
Typearticle
Languageen
FieldEngineering
TopicTribology and Wear Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceComposite materialCoatingThermal sprayingTinCarbon fiber reinforced polymerLayer (electronics)PolymerSurface roughnessCopperPlating (geology)Composite numberMetallurgy

Abstract

fetched live from OpenAlex

Abstract Carbon fiber (CF) composites are widely used in the aerospace industry due to their light weight and favorable mechanical properties. Nevertheless, applying protective coatings (e.g. erosion resistance) through thermal spraying presents specific challenges with defects such as distortion, oxidation, and poor coating adhesion. This study presents a new technique that combines electroless plating processes and thermal spray for depo-siting metals onto polymer-reinforced composites. Samples of low melting polyaryletherletone (LMPAEK) thermoplastic polymer reinforced with carbon fibers aligned in the normal direction (ZRT film) are plated (with copper, silver, or nickel) to provide an adhesion layer for the thermal spray processes. Subsequently, pure tin and titanium (i.e. Ti-6Al-4V) is deposited on the samples using High Velocity Air-Fuel (HVAF) and atmospheric plasma spray (APS) processes. Cross-sections of the resulting coatings are compared, and the materials are characterized for surface roughness, cracks and pores.

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 score0.030
Threshold uncertainty score0.410

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 head0.005
GPT teacher head0.191
Teacher spread0.187 · 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
Published2024
Admission routes1
Has abstractyes

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