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Record W2746669011

DESTRUCTIVE INTERFERENCES CREATED USING ADDITIVE MANUFACTURING

2017· article· en· W2746669011 on OpenAlexvenueno aff
Umberto Berardi

Bibliographic record

VenueCanadian acoustics · 2017
Typearticle
Languageen
FieldEngineering
TopicInnovations in Concrete and Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsAcousticsResonatorAbsorption (acoustics)Interference (communication)FabricationHelmholtz free energyMaterials sciencePorosityHelmholtz resonatorPorous mediumMechanical engineeringComputer scienceEngineeringComposite materialOptoelectronicsPhysicsTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

Current solutions to absorb sound have technical limitations in being tailored to the specific acoustic requirements of a space. In fact, porous materials show high absorption at higher frequency, while at low frequency, the use of porous materials often require an impracticable significant thickness. Alternatively, resonance absorption mechanisms (either Helmholtz resonators or vibrating panels) show low frequency absorption but in limited frequency ranges. This research focuses on absorbers created using additive manufacture in order to absorb in the low frequency rage using passive destructive interference principles. Additive manufacturing allows for the fabrication of unique pieces with complex and freeform curved geometries. Focusing on geometrical aspects, this paper presents the results of the investigation of an ongoing project to create a large thin transparent panel with absorption above 0.6 below 250 Hz with limited thickness.

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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.240
Teacher spread0.217 · 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

Citations6
Published2017
Admission routes1
Has abstractyes

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