Publication Cover
Transactions of the IMF
The International Journal of Surface Engineering and Coatings
Volume 100, 2022 - Issue 2
117
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Mathematical modelling of sound transmission loss (STL) in metallic and graphite based coatings

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 85-92 | Received 01 Jun 2021, Accepted 20 Sep 2021, Published online: 03 Feb 2022
 

ABSTRACT

This study aimed to investigate the relationship between the sound transmission loss (STL) properties and surface morphologies of metallic-based and graphite-based coatings. Aluminium (22 µm average particle size (APS) and 12 µm APS), copper (12 µm APS), silver (14 µm APS) as conductive metal pigments and graphite (18 µm APS) as a semi-conductive pigment were used to create coatings and STL properties were measured with an in-house designed sound wave modulation device. Laser beams with different carrier wavelengths were used in the experiment, for which the wavelength with the highest R2 and number of significant variables was used to create a mathematical model to help in measuring the sound transmission loss properties. Surface tension energy, conductivity, permeability, reflectivity, concentration and the type of pigment were found to be significant in determining the STL properties.

Acknowledgements

The authors thank the BAP Coordination Unit for their support (Project ID: FEN-C-DRP-170419-0129).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 303.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.