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Surface Roughness Effects on the Fatigue Behavior of As-Machined Inconel718

2016· article· en· W2560369407 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.

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2016
Typearticle
Languageen
FieldEngineering
TopicMechanical Failure Analysis and Simulation
Canadian institutionsPolytechnique MontréalUniversité LavalÉcole de Technologie Supérieure
Fundersnot available
KeywordsMaterials scienceSurface roughnessSurface finishResidual stressBendingComposite materialAmplitudeStructural engineeringOpticsEngineering

Abstract

fetched live from OpenAlex

Surface finish of machined components plays a key role in their life performance. The aim of this research is to investigate the effect of different roughness parameters on high cycle fatigue behavior of Inconel718. Rotating bending fatigue tests have been performed on Inconel718 specimens with various surface roughnesses produced by turning. Height and amplitude distribution roughness parameters were investigated. Statistical analyses show that a valley material component (Mr2), as one of the amplitude distribution parameters, is the most relevant parameter for the high cycle fatigue life of machined specimens. Observations conducted at the surface of broken specimens gage length, have shown the impact of surface roughness and residual stresses on the crack propagation mode. When the roughness increases, valleys were shown to be deeper and larger, leading to a higher Mr2 value and an increase of stress concentration.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.559
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.002
Research integrity0.0000.001
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 head0.038
GPT teacher head0.298
Teacher spread0.261 · 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