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Record W2692066856 · doi:10.1177/0263092317712017

Additive manufacturing for minimizing vibration damages in the water-stop tray of washing machines

2017· article· en· W2692066856 on OpenAlexafffund
Umberto Berardi, Gino Iannace

Bibliographic record

VenueJournal of low frequency noise, vibration and active control · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced machining processes and optimization
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTrayVibrationAccelerometerHammerImpulse (physics)EngineeringStructural engineeringAcousticsMechanical engineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

The present work aims to study the water-stop tray, a front-loading component of a washing machine. As most of the components of washing machines, the water-stop tray suffers significant vibrations especially when the spin speed increases. A vibrational analysis of the current typical design was realized. Then, four modified trays were investigated and a hammer impulse response was recorded using 13 accelerometers in order to measure the frequency response of each tray. The results allow to identify the key parameters that affect the vibrational response of a water-stop tray. Finally, the comparative analysis among the different designs allows to select the tray with the lowest vibration amplitude.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.718
Threshold uncertainty score0.305

Codex and Gemma teacher scores by category

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 communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.242
Teacher spread0.234 · 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 teacher head, 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
Published2017
Admission routes2
Has abstractyes

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