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Application of Acoustic Emission in Control of High-Pressure Pipe Overstraining

2006· article· en· W1969780475 on OpenAlexaff
M. Nowak, Ireneusz Baran, J. Schmidt, Z. Płochocki

Bibliographic record

VenueAdvanced materials research · 2006
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering Research and Applications
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsAcoustic emissionConsistency (knowledge bases)Process (computing)EngineeringAcousticsStructural engineeringComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

The article describes the application of AE technique in control and evaluation of the pipe overstraining process. The trials of overstraining were conducted according to a programme of pipe loading with internal pressure, developed and adjusted to the examined pipe material. In the studies two systems of acoustic emission were used, viz. PAC Mistras 2001 and Vallen AMSY. The procedure adopted in the development of evaluation criteria based on an analysis of the recorded acoustic events was described. Next, the criteria of evaluation were presented. The effectiveness of the applied method, the validity of the adopted criteria, and the consistency of the obtained quantitative results were checked during tests performed by other NDT methods. A comparative analysis was made to serve as a complementary tool in final evaluation of the pipe overstraining process done by AE technique.

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)0.002
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.296
Teacher spread0.284 · 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

Citations2
Published2006
Admission routes1
Has abstractyes

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