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Record W4403074690 · doi:10.1016/j.heliyon.2024.e38866

Induction cladding of alloys and metal-matrix composite coatings: A review

2024· review· en· W4403074690 on OpenAlexfundno aff
Jing Yu, Shuai Zhang

Bibliographic record

VenueHeliyon · 2024
Typereview
Languageen
FieldEngineering
TopicSurface Treatment and Coatings
Canadian institutionsnot available
FundersNatural Science Foundation of Liaoning ProvinceNational Natural Science Foundation of ChinaSt. Thomas UniversityShantou University
KeywordsComposite numberMaterials scienceCladding (metalworking)Metal matrix compositeMetallurgyMatrix (chemical analysis)MetalComposite material

Abstract

fetched live from OpenAlex

Induction cladding is a promising surface technology that combines the advantages of surface coatings and induction heating. It is an energy-efficient, environment-friendly, and cost-effective method that facilitates the fabrication of coatings with controllable thicknesses and ensures metallurgical bonding between the coating and the substrate. Owing to the high power-conversion efficiency of helical coil, induction cladding is particularly adaptable for the application of coatings on long shafts and rod parts, which find widespread use in mining and energy machinery. This paper provides a comprehensive overview of the state-of-the-art methods in induction cladding. Herein we focus on its mechanisms, cladding process and parameters, commonly used materials, simulations, innovative induction cladding technologies, industrial applications, problems, and future developments in this field.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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 head0.035
GPT teacher head0.314
Teacher spread0.278 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations8
Published2024
Admission routes1
Has abstractyes

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