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Record W4398200521 · doi:10.1080/00084433.2024.2355428

Fabrication of AlSi10Mg–AlN metal matrix composites using laser powder bed fusion technology

2024· article· en· W4398200521 on OpenAlexafffund
J. Comhaire, I.W. Donaldson, D.P. Bishop

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaResearch Nova Scotia
KeywordsFabricationMaterials scienceComposite materialFusionMetalLaserMatrix (chemical analysis)MetallurgyOptics

Abstract

fetched live from OpenAlex

AlSi10Mg has evolved to become a standard aluminium alloy utilised in additive manufacturing (AM), owing to a robust response to processing and desirable properties such as a high-strength-weight ratio. Despite these promising traits, the stiffness, thermal conductivity, and thermal stability of AM AlSi10Mg remain inadequate for certain applications. In an effort to bolster performance in these areas, coupling the alloy with controlled levels of ceramic particulate is under investigation. Specifically, research on the incorporation of aluminium nitride (AlN) additions and how these influence the processability of AlSi10Mg in the context of laser powder bed fusion has been considered. Using a design of experiments (DOE) approach, the effects of AlN concentration, laser power, scan speed, and hatch spacing on final part density were studied. The optimal range of VED was determined to be 60–80 J/mm3 for all chemistries considered. However, the presence of AlN was found to reduce as-built density as the ceramic concentration increased. AlN remained largely unaffected by laser irradiation.

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 categoriesInsufficient payload (model declined to judge)
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 score0.341
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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

Citations3
Published2024
Admission routes2
Has abstractyes

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