Robust design of an asymmetrically absorbing Willis acoustic metasurface subject to manufacturing-induced dimensional variations
Abstract
Advancements in additive manufacturing (AM) technology are promising for the creation of acoustic materials. Acoustic metamaterials and metasurfaces are of particular interest for the application of AM technologies as theoretical predictions suggest the need for precise arrangements of dissimilar materials within specified regions of space to reflect, transmit, guide, or absorb acoustic waves in ways that exceed the capabilities of currently available acoustic materials. This work presents the design of an acoustic metasurface (AMS) with Willis constitutive behavior, which is created from an array of multi-material inclusions embedded in an elastomeric matrix, which displays the asymmetric acoustic absorption. The finite element models of the AMS show that the asymmetric absorption is dependent on asymmetry in the distribution of materials within the inclusion and highly sensitive to small changes in the inclusion geometry. It is shown that the performance variability can be used to place constraints on the manufacturing-induced variability to ensure that an as-built AMS will perform using the as-designed parameters. The evaluation of the AMS performance is computationally expensive, thus, the design is performed with a classifier-based metamodel to support more efficient Monte Carlo simulations and quantify the sensitivity of the candidate design performance to the manufacturing variability. This work explores combinations of material choices and dimensional accuracies to demonstrate how a robust design approach can be used to help select AM fabrication methods or guide process development toward an AM process that is capable of fabricating acoustic material structures.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 01, 2022
- Source ID
- 10.1121/10.0009162
Entities
People
- Carolyn Conner Seepersad
- Michael R Haberman
- Tyler Wiest
Organizations
- National Science Foundation
- United States Naval Research Laboratory
- University of Texas at Austin