Low cost modelling tools for the design of additively manufactured acoustic materials

Additive manufacturing (AM) methods have unlocked new capabilities in the design of acoustic materials and their topologies. This has enabled the design of devices with breakthrough performance in sound absorption and insulation while achieving subwavelength characteristics. However, material fabrication through AM presents its own challenges. These challenges include inherent defects which can be specific to the printing method employed. Examples of defects in low-cost AM include void formation, surface roughness and poor bonding between layers. Each of these features of AM have different acoustic impacts which may degrade the performance of a design. To optimize the production of additively manufactured acoustic materials it is imperative that designers can exploit features, such as surface roughness, to enhance rather than degrade the overall material performance. The aim of this paper is to investigate the effects of surface roughness in extrusion-based AM on acoustic material performance. A novel, low-cost numerical methodology is used to capture the influence of surface roughness in a subwavelength acoustic absorber. With this approach the impact of roughness can be captured in the design process. The intention is to develop efficient low-cost design methodologies that can be exploited for industrial development of novel additively manufactured acoustic materials.

Authors

Institutions

Publication Details

Journal
NOISE-CON proceedings
Published
2024-10-04
DOI
https://doi.org/10.3397/in_2024_3337
Primary Topic
Innovations in Concrete and Construction Materials
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Low cost modelling tools for the design of additively manufactured acoustic materials

John F. Kennedy, Sophie WELDON
1 links
NOISE-CON proceedings
Innovations in Concrete and Construction Materials
article

Low cost modelling tools for the design of additively manufactured acoustic materials

John F. Kennedy, Sophie WELDON
article en

Abstract

Additive manufacturing (AM) methods have unlocked new capabilities in the design of acoustic materials and their topologies. This has enabled the design of devices with breakthrough performance in sound absorption and insulation while achieving subwavelength characteristics. However, material fabrication through AM presents its own challenges. These challenges include inherent defects which can be specific to the printing method employed. Examples of defects in low-cost AM include void formation, surface roughness and poor bonding between layers. Each of these features of AM have different acoustic impacts which may degrade the performance of a design. To optimize the production of additively manufactured acoustic materials it is imperative that designers can exploit features, such as surface roughness, to enhance rather than degrade the overall material performance. The aim of this paper is to investigate the effects of surface roughness in extrusion-based AM on acoustic material performance. A novel, low-cost numerical methodology is used to capture the influence of surface roughness in a subwavelength acoustic absorber. With this approach the impact of roughness can be captured in the design process. The intention is to develop efficient low-cost design methodologies that can be exploited for industrial development of novel additively manufactured acoustic materials.

NOISE-CON proceedingsVol. 270(8)
Trinity College Dublin (IE)
Openalex Percentile: Top 82%
Innovations in Concrete and Construction Materials
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.