MétaCan
Menu
Back to cohort
Record W2901032849 · doi:10.25071/10315/35390

Effect Of Loading Strain Rates On Unloading Behavior Of Shot Peened Materials

2018· article· en· W2901032849 on OpenAlex
Amir Yazdanmehr, Hamid Jahed

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

VenueProgress in Canadian Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicSurface Treatment and Residual Stress
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsShot (pellet)PeeningMaterials scienceStrain (injury)Composite materialMetallurgyMedicineResidual stressAnatomy

Abstract

fetched live from OpenAlex

Shot peening is a process widely used in industry to improve the fatigue life of materials through induced compressive residual stresses that retard crack initiation and growth. In the peening process, there are two stages: 1) loading: shot penetrating into target; and 2) unloading: shot rebounding from the target. The strain rates in the loading process are known to be in 10 5 -10 6 1/s range, having heavy impact on the materials' properties. However, the effect of the loading strain rates on the rebounding stage is not well studied. This paper aims to determine the effects of the loading strain rates on the unloading behavior of a material using FEM method. First, to better understand the material behavior, this study evaluates the loading-unloading responses of one element at high strain rates in different scenarios. Then, it obtains the strain rates during the loading and unloading for the different elements of a material being impinged by one shot. The results show that the unloading behavior of a material depends only on the loading equivalent plastic strain and the strain rate of the unloading step.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score0.914

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 head0.012
GPT teacher head0.267
Teacher spread0.255 · 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