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Record W4293053637 · doi:10.1142/s1793962323500162

Residual stress investigation of ceramic-shot-peened flange pin with finite element analysis

2022· article· en· W4293053637 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

VenueAdvances in Complex Systems · 2022
Typearticle
Languageen
FieldEngineering
TopicSurface Treatment and Residual Stress
Canadian institutionsNational Research Council CanadaCarleton University
Fundersnot available
KeywordsPeeningResidual stressFlangeShot peeningMaterials scienceFinite element methodCurvatureShot (pellet)Structural engineeringComposite materialMetallurgyGeometryEngineering

Abstract

fetched live from OpenAlex

The flange pin of landing gears, which is made of high strength low alloy, is peened with ZirShot-ZC600 ceramic shots. The residual stresses generated in the peened flange pin, which will affect the fatigue performance of the pin, are investigated with finite element analysis (FEA). Three-dimensional FEA model is created to simulate the target surface and shot peening flow which is generated randomly using the MATLAB program. It is revealed that the residual stresses of the peened surface layer in longitudinal, circumferential and radial direction all vary in depth from the target surface and they are all compressive with the maximum compressive stresses occurring at subsurface. The effects of target surface curvature and shot peening angle on the residual stress profiles in the peened surface are also studied. The FEA simulation results show that the maximum residual stresses in flat surface are greater than that in the surface with curvature and with increasing peening angle, the location of the maximum residual stresses moves down in depth from the peened surface.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.026
GPT teacher head0.250
Teacher spread0.224 · 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