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Record W3093529060 · doi:10.1139/tcsme-2020-0171

Experimental investigation on post-processed NiCr thermal barrier coating and its sliding wear behaviour

2020· article· en· W3093529060 on OpenAlexvenueno aff
V. Srinivasan, P. Karuppuswamy, T. Velmurugan, G. Suganya Priyadharshini

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsNichromeMaterials scienceCoatingPorosityMetallurgyThermal sprayingComposite materialBase metalAdhesive wearMartensitic stainless steelMartensiteWear resistanceMicrostructureWelding

Abstract

fetched live from OpenAlex

NiCr metal is deposited on martensitic stainless steel using an atmospheric plasma spray method. To enrich the metallurgical properties of the NiCr metallic coating, a standard heat treatment process is adopted. From the investigation, it has been shown that the voids and porosity developed during the thermal spray have been controlled through heat treatment. Electron image analysis reveals that the coating has reduced porosity with strong and dense bonding strength. With reference to the surface hardness, the post-processed coating yields a maximum of 140 Hv compared with the coated (129 Hv) and base metal (115 Hv). Subsequently, the sliding wear behaviour of post-processed NiCr coating has a minimum wear of 25 μm for an applied load of 5 N and 57 μm for an applied load of 15 N. Owing to the high metallurgical bonding, the coating has sustained the heavy loads, and the wear formation is controlled. However, the base metal has an adhesive wear mechanism caused by the high frictional force on the sliding friction. Therefore, it is recommended that post-processed thermal barrier coatings possess good metallurgical bonding and withstand heavy load causing minimum wear.

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 score0.049
Threshold uncertainty score0.753

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 head0.018
GPT teacher head0.207
Teacher spread0.188 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicHigh-Temperature Coating BehaviorsFrench-language works237,207