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Record W2747605504 · doi:10.3390/coatings7080122

Development of Sustainable Cold Spray Coatings and 3D Additive Manufacturing Components for Repair/Manufacturing Applications: A Critical Review

2017· review· en· W2747605504 on OpenAlexafffund
Sunil Pathak, Gobinda C. Saha

Bibliographic record

VenueCoatings · 2017
Typereview
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaNew Brunswick Innovation Foundation
KeywordsGas dynamic cold sprayThermal sprayingMaterials scienceCermetResidual stressCeramicDeposition (geology)MetallurgyRemanufacturingProcess engineeringMechanical engineeringManufacturing engineeringNanotechnologyEngineeringCoating

Abstract

fetched live from OpenAlex

This review article presents the findings of a comprehensive state-of-the-art literature review of the scientific and technological progress of the cold spray process in the field of repair/remanufacturing using the concept of additive manufacturing. A thorough study was conducted on the potential of this technology to form (a) both thin and thick coatings; (b) the ability to fabricate 3D freeform components in a single process, while benefiting from reduced residual stress level compared to conventional thermal spray coatings processes such as high velocity oxy-fuel (HVOF) or plasma spraying. A systematic overview of the process technology, particularly focusing on the suitability of ceramic-metallic (cermet) composite particles used as feedstock in the deposition was conducted; further elaboration was made pertinent to particle impact and bonding mechanisms during the deposition.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.058
GPT teacher head0.334
Teacher spread0.276 · 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

Citations172
Published2017
Admission routes2
Has abstractyes

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