MétaCan
Menu
Back to cohort
Record W2152226291 · doi:10.1177/1045389x11404956

The Effect of Adhesive Layers on the Dynamic Behavior of Surface-bonded Piezoelectric Sensors with Debonding

2011· article· en· W2152226291 on OpenAlexafffund
Congrui Jin, Xiaodong Wang

Bibliographic record

VenueJournal of Intelligent Material Systems and Structures · 2011
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePiezoelectricityAdhesiveComposite materialPiezoelectric sensorLayer (electronics)Stress (linguistics)Structural engineeringEngineering

Abstract

fetched live from OpenAlex

The behavior of a piezoelectric sensor is strongly affected by the bonding condition along the interface between the sensor and the host structure. This article presents an analytical study of the effect of the mechanical and geometrical properties of the adhesive layer on the coupled dynamic electromechanical behavior of a thin piezoceramic sensor bonded to an elastic medium. A sensor model with an imperfect adhesive bonding layer is proposed to simulate the two dimensional electromechanical behavior of the integrated system. The analytical solution of the problem is provided by solving the resulting integral equations in terms of the interfacial stress. Numerical simulation is conducted to study the effect of the bonding layer upon the dynamic response of the sensor under different loading frequencies. The interfacial debonding and its effect on the interlaminar strain and stress transfer mechanisms are discussed in detail.

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)0.001
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 score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.008
GPT teacher head0.199
Teacher spread0.192 · 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

Citations9
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Intelligent Material Systems and StructuresSame topicUltrasonics and Acoustic Wave PropagationFrench-language works237,207