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Record W3116605601 · doi:10.1111/ffe.13406

Very high cycle fatigue characterization of additively manufactured AlSi10Mg and AlSi7Mg aluminium alloys based on ultrasonic fatigue testing

2020· article· en· W3116605601 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueFatigue & Fracture of Engineering Materials & Structures · 2020
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceAluminiumMetallurgyFatigue limitContext (archaeology)Fatigue testingTension (geology)Aluminium alloyComposite materialUltimate tensile strength

Abstract

fetched live from OpenAlex

Abstract This paper investigates long fatigue life characteristics of AlSi10Mg and AlSi7Mg aluminium alloys produced using direct metal laser sintering. An in‐house built ultrasonic fatigue testing machine was used to carry out fatigue tests on specimens machined from heat‐treated AlSi10Mg and AlSi7Mg alloys subjected to constant fully reversed tension–compression loadings. The fatigue life data acquired for these two additive manufacturing (AM) aluminium alloys were compared with previously published data under heat treatment and non‐heat treatment conditions. It was found that fatigue data of tested specimens fit more closely the data of specimens without any heat treatment. Fatigue test results indicate that effects of heat treatment on high cycle fatigue (HCF) and very high cycle fatigue (VHCF) performance of AM alloys should be further investigated in the context of the microstructural changes.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.249
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
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 head0.017
GPT teacher head0.210
Teacher spread0.193 · 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