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Record W2801006491 · doi:10.3390/jmmp2020025

The Static and Fatigue Behavior of AlSiMg Alloy Plain, Notched, and Diamond Lattice Specimens Fabricated by Laser Powder Bed Fusion

2018· article· en· W2801006491 on OpenAlexaff
Hugo Soul, Patrick Terriault, Vladimir Braïlovski

Bibliographic record

VenueJournal of Manufacturing and Materials Processing · 2018
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsMaterials scienceFabricationMicrostructureAlloyUltimate tensile strengthDiamond cubicLattice (music)DiamondFusionComposite materialUniaxial tension

Abstract

fetched live from OpenAlex

The fabrication of engineered lattice structures has recently gained momentum due to the development of novel additive manufacturing techniques. Interest in lattice structures resides not only in the possibility of obtaining efficient lightweight materials, but also in the functionality of pre-designed architectured structures for specific applications, such as biomimetic implants, chemical catalyzers, and heat transfer devices. The mechanical behaviour of lattice structures depends not only the composition of the base material, but also on the type and size of the unit cells, as well as on the material microstructure resulting from a specific fabrication procedure. The present work focuses on the static and fatigue behavior of diamond cell lattice structures fabricated from an AlSiMg alloy by laser powder bed fusion technology. In particular, the specimens were fabricated with three different orientations of lattice cells—[001], [011], [111]—and subjected to static tensile testing and force-controlled pull–pull fatigue testing up to 1 × 107 cycles. In parallel, the mechanical behavior of dense tensile plain and notched specimens was also studied and compared to that of their lattice counterparts. Results showed a significant effect of the cell orientation on the fatigue lives: specimens oriented at [001] were ~30% more fatigue-resistant than specimens oriented at [011] and [111].

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 score0.240
Threshold uncertainty score0.545

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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

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