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Record W2189279259

Oberst Beam Excitation Using Piezo Electric Actuators

2000· article· en· W2189279259 on OpenAlexaffvenue
Jean-Luc Wojtowicki

Bibliographic record

VenueCanadian acoustics · 2000
Typearticle
Languageen
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsBeam (structure)CantileverLaser Doppler vibrometerTransducerMaterials scienceExcitationShakerAcousticsActuatorTachometerStructural engineeringOpticsVibrationElectrical engineeringComposite materialEngineeringOptoelectronicsPhysics
DOInot available

Abstract

fetched live from OpenAlex

The «Oberst beam» is a classical method for the characterization of damping material based on a multilayer cantilever beam (base beam + one or two layers of other materials). As the base beam is made of a rigid and lightly damped material (steel, aluminum), the most critical aspect of this method is to properly excite the beam without adding weight or damping. So, exciting the beam with a shaker is not recommended because of the added mass. Alternative solutions are suggested such as electro-magnetic noncontacting transducer (tachometer pick-up, for example) can provide a good excitation but it is limited to ferro-magnetic materials. As aluminum and stainless-steel are widely used for the base beam, a small bits of magnetic material must be fastened adhesively to achieve specimen excitation. This method creates two other problems. The first one is the difficulty to properly measure the excitation force and the second one is the added damping due to the magnetic materials bits in the case of non-magnetic base beam. However, the measurement of the motion of the beam can be easily made using a non-contact transducer (a laser vibrometer for example).

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: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.021
GPT teacher head0.218
Teacher spread0.197 · 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
Published2000
Admission routes2
Has abstractyes

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