Acoustic measurement and statistical characterization of direct-printed, variable-porosity aluminum foams

Abstract

Additive manufacturing has expanded greatly in recent years with the promise of being able to create complex and custom structures at will. Enhanced control over the microstructure properties, such as percent porosity, is valuable to the acoustic design of materials. In this work, aluminum foams are fabricated using a modified powder bed fusion method, which enables voxel-by-voxel printing of structures ranging from fully dense to approximately 50% porosity. To understand the acoustic response, samples are measured in an acoustic impedance tube and characterized with the Johnson–Champoux–Allard–Lafarge model for rigid-frame foams. Bayesian statistical inversion of the model parameters is performed to assess the applicability of commonly employed measurement and modeling methods for traditional foams to the additively manufactured, low porosity aluminum foams. This preliminary characterization provides insights into how emerging voxel-by-voxel additive manufacturing approaches could be used to fabricate acoustic metal foams and what could be learned about the microstructure using traditional measurement and analysis techniques.

Document Details

Document Type
Pub Defense Publication
Publication Date
Jun 01, 2021
Source ID
10.1121/10.0005273

Entities

People

  • Charles A. Rohde
  • Christina J. Naify
  • Ryohei Gotoh
  • Scott Roberts
  • Stephanie G. Konarski

Organizations

  • California Institute of Technology
  • Office of Naval Research
  • United States Naval Research Laboratory

Tags

Fields of Study

  • Materials science
  • Physics

Readers

  • Manufacturing Engineering.
  • Medical Imaging.
  • Reinforced Composite Materials

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference