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Record W4387056111 · doi:10.3390/engproc2023043037

Metal Fused Filament Fabrication of AlSi10Mg Aluminum Alloy

2023· article· en· W4387056111 on OpenAlexafffund
Enrique Galindo, Milan Marić, Abraham Avalos Postigo, Alan Walker, Michael Conlon, Kamran Azari, Mathieu Brochu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsMcGill University
FundersMcGill University
KeywordsMaterials scienceFabricationFused filament fabricationExtrusionAlloyThermoplasticPolyesterProtein filamentAluminiumSinteringMetal powderComposite materialMetalMetallurgyPolymer

Abstract

fetched live from OpenAlex

Metal-fused filament fabrication (MF3), which is a variation of the conventional fused filament fabrication (FFF), has recently gained interest due to its distinctive process flexibility and rapid prototyping capability to produce metallic parts. With respect to the additive manufacturing (AM) of aluminum alloys, most efforts have been centered on laser powder bed fusion technologies, with limited activities focused on binder-based processes due to challenges in the sintering of aluminum powders. With respect to MF3, one challenge for fabricating metallic components is the appropriate selection of a binder mixture, enabling the extrusion of a filament with high metallic volume content. In this paper, a mixture of biodegradable aliphatic polyester thermoplastic is used as a binder phase to act as the carrier for AlSi10Mg alloy powders.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.228
Teacher spread0.208 · 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 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

Citations1
Published2023
Admission routes2
Has abstractyes

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