Acoustic metamaterials (AMMs) are an exciting technology because they are capable of responding to vibrations in ways that are impossible to achieve with conventional materials. However, realization of AMMs requires engineering design to provide a connection between first-principles research and production of parts that perform as expected. Designing AMMs is a challenging endeavor because evaluating designs is costly and manufacturing metamaterials requires precise techniques with small minimum resolutions. To address these challenges, new computational tools are necessary to aid design. This work proposes three tasks that improve the capabilities of design for AMM while being extensible to other engineering design automation tasks. The first task is to develop a design exploration tool that improves the computational efficiency of identifying sets of high-performing designs in a design space that is sparse and comprises mixed discrete/continuous data. The second task is to develop a process for designers to evaluate manufacturability of difficult-to-manufacture parts and drive co-development of manufacturing methods and AMM. In the final task, a machine learning based method is developed to efficiently model AMM with heterogeneous arrangements of their microstructures such that strict homogenization is infeasible. The outcomes from completing these tasks will provide a significant and novel improvement over existing methods of designing AMMs.Mechanical Engineerin
