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Record W7132981442

Hybrid Electromagnetic Acoustic Transducer (EMAT) Systems for the Non-Destructive Detection of Corrosion-Induced Defects in Steel

2023· dissertation· W7132981442 on OpenAlexaff
Keith Sebastian

Bibliographic record

VenueTSpace · 2023
Typedissertation
Language
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectromagnetic acoustic transducerTransducerSIGNAL (programming language)Finite element methodMagnetostrictionElectromagnetic coilReduction (mathematics)Transmitter
DOInot available

Abstract

fetched live from OpenAlex

Four combinations of Electromagnetic Acoustic Transducers operated at two frequencies are tested on an industrially representative 1018 Low Carbon steel plate and pipe section with an artificial pitting defect. The combination involving a Periodic Permanent Magnet EMAT as the transmitter and a Magnetostrictive Meander Line Coil EMAT as the receiver was found to be the most favourable due to its strong signal strength in non-defect regions and signal reduction in areas with defects. It was also generally found that operating at the lower of a pair of operating frequencies and using the Magnetostrictive EMAT as the receiver led to better overall defect sensitivity. Two of the four combinations were hybrid versions, which demonstrated heightened signal strength over their homogeneous counterparts. Finite element simulations and additional experiments were conducted to theoretically supplement and verify the above results and trends.

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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0010.000
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 head0.259
Teacher spread0.247 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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