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Record W3106127632 · doi:10.3390/jmmp4040107

Influence of Powder Particle Morphology on the Static and Fatigue Properties of Laser Powder Bed-Fused Ti-6Al-4V Components

2020· article· en· W3106127632 on OpenAlexaff
Salah Eddine Brika, Vladimir Braïlovski

Bibliographic record

VenueJournal of Manufacturing and Materials Processing · 2020
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMaterials sciencePorosityParticle (ecology)Composite materialParticle sizeMetal powderFusionPerpendicularPowder metallurgyElongationMetallurgyMicrostructureUltimate tensile strengthChemical engineering

Abstract

fetched live from OpenAlex

In this work, two Ti-6Al-4V powder lots were produced using two different techniques: plasma atomization and gas atomization, with the first producing more spherical particles than the second. Testing specimens were then manufactured with these powder lots using an identical set of printing parameters and the same laser powder bed fusion system. Next, the porosity levels and distributions as well as the static and fatigue properties of the specimens from both powder lots were compared. Regarding the static mechanical properties, a noticeable difference was observed between the plasma-atomized powder specimens and their gas-atomized equivalents (7% greater ultimate and 4% greater yield strengths, but 3% lower elongation to failure, respectively). However, with regard to the fatigue resistance, the advantages of the plasma-atomized powder specimens in terms of their mechanical resistance were somewhat counterbalanced by the presence of pores aligned in the direction perpendicular to that of applied load. Conversely, specimens printed with the gas-atomized powder manifested a similar level of porosity, but a uniform pore distribution, which reduced the impact of the processing-induced porosity on fatigue cracks initiation and propagation.

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 score0.006
Threshold uncertainty score0.463

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 head0.037
GPT teacher head0.224
Teacher spread0.188 · 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

Citations11
Published2020
Admission routes1
Has abstractyes

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