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Cold spray - a solid-state additive manufacturing technology

2024· article· en· W4401557847 on OpenAlexaff
Congcong Su, Yan Wang, Hanqing Che, Stephen Yue, Xiaoxu Huang

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2024
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsMcGill University
Fundersnot available
KeywordsGas dynamic cold sprayMicrostructureFabricationMaterials scienceComponent (thermodynamics)Deposition (geology)Solid-stateProcess (computing)Process engineeringSpray dryingManufacturing engineeringMetallurgyNanotechnologyComputer scienceEngineeringEngineering physicsCoatingChemical engineeringGeology

Abstract

fetched live from OpenAlex

Abstract Cold spray is a solid-state powder deposition technique and has evolved into an additive manufacturing process. Unlike conventional additive manufacturing technologies that rely on melting and solidification, cold spray additive manufacturing (CSAM) forms components at low temperatures at a relatively high build rate. This article introduces the technical principles, process parameters and typical microstructure of cold spray, as well as its applications in the fabrication of 3D components and damaged component repair. Current issues faced in cold spray research and future development directions in CSAM are also discussed.

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 categoriesMeta-epidemiology (narrow)
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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.

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
Published2024
Admission routes1
Has abstractyes

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