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Record W2004123278 · doi:10.1115/omae2012-83469

Underwater Stress Relief and Fatigue Improvement by Ultrasonic Peening

2012· article· en· W2004123278 on OpenAlexaff
Jacob I. Kleiman, Yuri Kudryavtsev, Alexander Lugovskoy

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicMaterial Properties and Applications
Canadian institutionsIntegrity Testing Laboratory (Canada)
Fundersnot available
KeywordsPeeningUnderwaterResidual stressUltrasonic sensorWeldingMaterials scienceTransducerUltimate tensile strengthShot peeningStress (linguistics)Fatigue limitStructural engineeringAcousticsComposite materialEngineeringGeology

Abstract

fetched live from OpenAlex

A new ultrasonic peening instrument was developed for underwater treatment of welds and welded structures. The Ultrasonic Peening (UP) established itself as a promising process for fatigue life improvement of welded elements and structures. The beneficial effect of UP is obtained through relieving of harmful tensile residual stresses and introducing of compressive residual stresses into surface layers of materials, and through decreasing of stress concentration in weld toe zones and enhancement of mechanical properties of the surface layers of the materials. In the design of the underwater UP instrument specially selected anti-corrosion materials are used. The underwater UP instrument can be used for treatment of welds at depths up to 30 meters or, if required, with certain modifications, even deeper. Acoustic pump principle is used in the originally developed system for water cooling of the transducer. The developed UP system allows for improvement treatments at four different power levels and is using replaceable working heads that come in various configurations with variable numbers of pins, depending on the application.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.250
Teacher spread0.228 · 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

Citations3
Published2012
Admission routes1
Has abstractyes

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