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Record W2582502833 · doi:10.1080/02670844.2016.1258769

Heat treatment effect on wear behaviour of HVOF-sprayed near-nanostructured coatings

2017· article· en· W2582502833 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSurface Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced materials and composites
Canadian institutionsAlberta InnovatesUniversity of Calgary
FundersAlberta Innovates
KeywordsMaterials scienceAbrasiveThermal sprayingCoatingMicrostructureMetallurgyBrittlenessIndentationComposite materialIndentation hardnessWear resistanceNichrome

Abstract

fetched live from OpenAlex

This study investigates the effect of heat treatment on changes in microstructure and wear behaviour of WC-NiCr coatings. Two feedstock powders with a similar chemical composition and different particle sizes (near nano-structured WC-17NiCr and microstructured WC-15NiCr) were used. High-velocity oxyfuel spraying technique was used to deposit coatings on to a mild steel substrate using identical spraying parameters. Coated samples were then heat treated in a nitrogen atmosphere at 500 and 700°C. The effect of heat treatment on changes in hardness and wear performance of the coatings was studied using microstructural analysis, micro-hardness indentation and abrasive wear tests. The results showed that the heat treatment increased the hardness of both coatings and a corresponding increase in wear resistance was recorded. The formation of a brittle CrWO 4 phase in the microstructured coating resulted in brittle fracture of the coating and this gave lower wear resistance compared to the nanostructured coatings.

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.

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 score0.351
Threshold uncertainty score0.941

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 head0.215
Teacher spread0.210 · 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