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Record W4400287215 · doi:10.1121/10.0027668

Robotic fabrication of clay acoustic resonators

2024· article· en· W4400287215 on OpenAlexaffabout
Brady Peters, Nicholas Hoban, John Nguyen, Nermine Hassanin

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2024
Typearticle
Languageen
FieldEngineering
TopicModular Robots and Swarm Intelligence
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsFabricationResonatorAcousticsMaterials scienceGeologyOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Digital fabrication offers the potential for the creation of complex geometries and mass-customized products; however, most 3D printers do not scale sufficiently to create architectural scale components. Robotic fabrication methods may bridge the gap, offering the possibility of architectural-scale 3d-printing capabilities. It has been found that the combination of multiple Helmholtz resonators tuned to different frequencies can create broadband absorption. This research pairs CAD parametric design with robotic clay extrusion as a method of acoustic resonator mass customization. The history of architectural acoustics together with recent archeological discoveries unveils a long-established practice of using clay vases as acoustic devices. And while the efficacy of these vases in historical settings has been contested, the use of large arrays of carefully tuned acoustic vases remains largely unexplored in contemporary practice. This paper presents the acoustic vases' unique history, defines its geometry and performance, and projects the potentials of the acoustic vase in current practice through modelling, simulation, and fabrication. A full-scale prototype wall with 126 robotically 3d-printed clay resonator vases was designed and constructed. The 1:1 prototype was shown in the “Robotic Clay,” which was exhibited at the Canadian Clay and Glass Gallery in Waterloo, Canada.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.227

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 communication0.0000.000
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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueThe Journal of the Acoustical Society of AmericaSame topicModular Robots and Swarm IntelligenceFrench-language works237,207