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Record W2792542240 · doi:10.1002/pamm.201710181

Laser shock peening process modelling and experimental validation of AA2198‐T3 and AA2198‐T8

2017· article· en· W2792542240 on OpenAlexaff
Sören Keller, Nikolai Kashaev, Benjamin Klusemann

Bibliographic record

VenuePAMM · 2017
Typearticle
Languageen
FieldEngineering
TopicSurface Treatment and Residual Stress
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsPeeningResidual stressMaterials scienceLaser peeningShock (circulatory)AutofrettageShot peeningLaserFinite element methodResidualSheet metalStructural engineeringComposite materialOpticsComputer scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Laser shock peening (LSP) is a surface treatment which improves the fatigue life of metallic structures by laser‐induced compressive residual stresses. The purpose of this work is the prediction of the measured residual stresses after LSP in sheets consisting of AA2198. Therefore, a finite element model is set‐up. The Johnson‐Cook material model is used to model the strain rate depend material behaviour during plastic deformations. Stress averaging enables the comparison between measured and predicted residual stresses. Residual stress measurements are performed using the incremental hole drilling method. The initially unknown pressure pulse is identified for AA2198‐T3 and validated with AA2198‐T8. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.264
Teacher spread0.240 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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