Proceedings Article | 2 October 2024
KEYWORDS: Metamaterials, Acoustics, Materials properties, Structural design, Machine learning, Evolutionary algorithms, Acoustic waves, Voxels, Visualization
In the realm of metamaterial research, the exploration of random structures presents an innovative path less traveled, compared to the conventional focus on periodic designs. Our study introduces a novel framework for generating random metamaterials using graph algorithms, which ensures connectivity and adaptability across a multitude of base shapes, such as cylinders, triangles, pyramids, and cubes. This flexibility enables the application of our designs across various domains, allowing for the investigation of properties including stiffness, density, and acoustic impedance. By leveraging graph algorithms in our framework, data representation and manipulation become more intuitive and efficient, facilitating the design process. Our approach demonstrates significant versatility in manipulating the macroscale and microscale elements of the designs, providing a tailored fit for specific applications. We present a series of designs, showcasing the ability to control and predict the material’s behavior under different conditions. The designs can be effectively implemented across various fields and subjected to multiple analytical studies, encompassing static, dynamic, and eigenfrequency assessments. Properties such as impedance, stiffness, density, and more can be explored, opening the door to a wide array of applications and potential innovations in metamaterial research. We illustrate the computational results for stiffness and acoustic impedance, highlighting the method’s efficacy through examples ranging from rod-based to cube-based designs. This framework not only paves the way for advancements in metamaterial research but also opens up new possibilities for innovation in fields requiring customized material properties.
