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Record W4402154386 · doi:10.31399/asm.cp.itsc2024p0732

Cold-Sprayed Aluminum Alloys: Exploring the Differences Between 6061 and 7075

2024· article· en· W4402154386 on OpenAlexaff
Kimia Rafiee, Evelin Barbosa de Mélo, Phuong Vo, M. Martín, S. Yue

Bibliographic record

VenueThermal spray · 2024
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsNational Research Council CanadaMcGill University
Fundersnot available
KeywordsMaterials scienceGas dynamic cold sprayAlloyDuctility (Earth science)MetallurgyDeposition (geology)AluminiumMicrostructureAlonizingSpray formingWork (physics)Composite materialCreepThermodynamicsCoating

Abstract

fetched live from OpenAlex

Abstract Cold spray is a solid-state metal powder deposition technique that has proven to be highly effective in depositing a wide range of metals, including aluminum and its alloys. However, higher strength, heat treatable Al alloys appear to exhibit variable deposition efficiencies and responses to heat treatments designed to increase ductility. This work is aimed at understanding the sources of these variabilities. In this study, 6061 (0.9% Mg, 0.6% Si, 0.3% Cu, 0.1% Cr, 0.1% Fe) alloy is compared to 7075 (6% Zn, 1.6% Cu, 2.4% Mg, 0.2% Cr, 0.3% Fe) alloy. These are common heat treatable alloys, but they exhibit quite different cold spray characteristics. Generally, 7075 is more problematic in terms of deposition efficiency and the mechanical properties after heat treatment. The alloys were processed under various cold spray conditions, including laser assisted cold spray designed to soften the 7075, and subjected to heat treatments intended to increase ductility. The microstructure and mechanical properties of the as sprayed and heat-treated coatings were characterized and compared. The results of this investigation will reveal possible mechanisms explaining the different cold spray behaviors and some suggestions will be proposed to overcome the problems associated with 7075.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.581
Threshold uncertainty score0.609

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 head0.034
GPT teacher head0.234
Teacher spread0.200 · 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
Published2024
Admission routes1
Has abstractyes

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