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Record W2765161638 · doi:10.1080/02670844.2017.1391939

Effect of shot peening coverage on residual stress field and surface roughness

2017· article· en· W2765161638 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

VenueSurface Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicSurface Treatment and Residual Stress
Canadian institutionsCarleton University
FundersChina Scholarship CouncilSouthwest Jiaotong UniversityNational Natural Science Foundation of China
KeywordsResidual stressShot peeningMaterials scienceSurface roughnessPeeningShot (pellet)Surface finishResidualMetallurgyComposite materialStress fieldFinite element methodStructural engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

Surface coverage is an important parameter in the shot peening process, exerting a significant influence on the residual stress field and surface roughness. In this study, a random-shots finite element model and a new coverage calculation method are employed to study the effect of shot peening coverage on the residual stress field and surface roughness of Q345qD steel. In addition, a shot peening experiment using a Q345qD steel specimen is designed to verify the simulation results. The results show reasonable agreement between the simulated values of the residual stress and roughness and those measured experimentally on the specimen. The obtained results revealed that coverage has significant influences on the residual stress field and surface roughness. In the actual shot peening process, engineers need to pay more attention to the stage of shot peening where coverage approaches 100%.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score1.000

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 head0.009
GPT teacher head0.241
Teacher spread0.232 · 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