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Record W7131998797

Cold spray technology for transportation applications: a process with impact

2020· article· en· W7131998797 on OpenAlexvenueno aff
D. Poirier, P. Vo, F. Nadeau, B Guerreiro, J. G. Legoux, E. Irissou

Bibliographic record

VenueNPARC · 2020
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsnot available
Fundersnot available
KeywordsGas dynamic cold sprayThermal sprayingCoatingWeldingAluminiumSpray nozzleRotor (electric)Flexibility (engineering)Decarburization
DOInot available

Abstract

fetched live from OpenAlex

Cold Spray is an emerging thermal spray technology where powders are accelerated at supersonic velocities onto the surface to be coated, whereupon they consolidate and bond instantly on impact. This manufacturing process can produce few microns to several centimeters’ thick deposits from a variety of materials displaying a wide range of properties. This paper presents several case studies to illustrate how cold spray flexibility can lead to several promising applications within the transportation industry. Firstly, as a coating process, it can enhance performance of light metals. The specific example of a bilayer steel-based coating for the production of lightweight Al brake rotor discs that show unmatched adhesion and thermal cycling resistance with wear and frictional performance equivalent to cast iron will be presented. As an additive manufacturing method, it will be shown that cold spray can build topologically optimized structural reinforcement on large aluminum panels for surface transportation. Finally, as a localized metal addition technique, its capability to add transition layers to allow the welding of dissimilar materials will be demonstrated for the friction stir welding of aluminum and high strength steel.

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 score0.046
Threshold uncertainty score0.334

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 head0.008
GPT teacher head0.248
Teacher spread0.239 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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