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Protection of 310l Stainless Steel from Wear at Elevated Temperatures using Conicraly Thermal Spray Coatings with and without Sic Addition

2017· article· en· W2765869175 on OpenAlexafffund
Yan Zhang, Tao Zhang, Kaiyang Li, Dongyang Li

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Alberta
FundersNanjing Institute of TechnologyUniversity of Alberta
KeywordsMaterials scienceThermal sprayingCoatingMicrostructureMetallurgyWear resistanceComposite material

Abstract

fetched live from OpenAlex

Due to its high oxidation resistance, 310L stainless steel is often used for thermal facilities working at high-temperatures. However, the steel may fail prematurely at elevated temperatures when encounter surface mechanical attacks such as wear. Thermal spray coatings have been demonstrated to be effective in protecting the steel from wear at elevated temperatures. In this study, we investigated the effectiveness of high velocity oxy-fuel(HVOF) spraying CoNiCrAlY/SiC coatings in resisting wear of 310L stainless steel at elevated temperature using a pin-on-disc wear tester. In order to further improve the performance of the coating, 5%SiC was added to the coating. It was demonstrated that the CoNiCrAlY/SiC coating after heat treatment markedly suppressed wear. However, the added SiC particles did not show benefits to the wear resistance of the coating. Microstructures of CoNiCrAlY coatings with and without the SiC addition were characterized in order to understand the mechanism responsible for the observed phenomena.

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 score0.023
Threshold uncertainty score0.917

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 studies0.0000.001
Scholarly communication0.0010.001
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 head0.017
GPT teacher head0.221
Teacher spread0.204 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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