MétaCan
Menu
Back to cohort

Influence of mean stress and building orientation on the fatigue properties of sub-unital thin-strut miniaturized Ti6Al4V specimens additively manufactured via Laser-Powder Bed Fusion

2023· article· en· W4389514150 on OpenAlexaff
Simone Murchio, Anton du Plessis, Valerio Luchin, Devid Maniglio, M. Benedetti

Bibliographic record

VenueInternational Journal of Fatigue · 2023
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsObject Research Systems (Canada)
FundersUniversità degli Studi di TrentoMinistero dell’Istruzione, dell’Università e della RicercaDipartimenti di Eccellenza
KeywordsMaterials scienceTitanium alloyFatigue testingFatigue limitFusionSurface roughnessStress (linguistics)Surface finishBucklingLattice (music)Composite materialStructural engineeringAlloy

Abstract

fetched live from OpenAlex

Fatigue is a complex, localized phenomenon affecting lattice structures at the level of struts and junctions. This study examines the fatigue properties of Laser-Powder Bed Fusion (L-PBF) Ti6Al4V miniaturized strut-like specimens, printed in four distinct building orientations (0°, 15°, 45°, 90°), and subjected to four stress ratios (R=0.1, R=-1, R=-4, R=10). Experimental data, summarized in Haigh diagrams, align well with predictions from the SWT and Walker methods. Notably, the dependency on mean stress diminishes as the building angle increases, attributed to progressively lower surface roughness which primarily drive failure at R=0.1 and R=-1. In contrast, vertical specimens at R=-4 display a pronounced susceptibility to sub-surface defects affecting their fatigue strength, a finding corroborated by CT scan analyses. Additionally, the influence of buckling on fatigue performance is investigated and incorporated into the Haigh diagrams. These findings can contribute to a more informed design of L-PBF Ti6Al4V lattice structures against fatigue.

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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 head0.024
GPT teacher head0.250
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 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

Citations36
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of FatigueSame topicAdditive Manufacturing Materials and ProcessesFrench-language works237,207