DDForms

Motivation

‍In this project, we aim to reduce the vibration level of finite-size elastic plates by integrating mechanical resonators. This approach - based on locally-resonant metamaterials design - offers lightweight, economical, and flexible vibration reduction for high-end applications such as precision machinery and aerospace components. By using machine learning, we wish to bypass the costly design procedure used normally.

Proposed Approach / Solution

‍We tackle two problems in this project: 1) Positioning and designing multiple resonators on a host structure such that response to vibrations is minimized (Figure 1). 2) Designing a resonator, that dampens predefined frequencies (Figure 2). For the first problem, we use a sampling technique called simulated annealing to find the optimal positioning. This is a stochastic optimization algorithm, that adds, removes, and moves resonators on the host-structure randomly. Changes that dampen the vibrations are more likely to be accepted. In this way, the configuration gets incrementally improved, while still allowing the exploration of many different configurations. For the second problem, we use a conditional variational autoencoder (cVAE). A cVAE is composed of an encoder and a decoder (Figure 3). The encoder maps the resonator design into a latent space. The decoder takes this mapping, and additionally specific user-specified resonant properties, and outputs a new design. With the trained model, the decoder can be used to propose many new designs with on-demand resonant properties, that can be 3D printed and tested for their actual resonant properties (Figure 4).

Impact

‍We find data-driven techniques that allow us to design materials that are less prone to be damaged by resonant frequencies. This can help with several problems, including manufacturing materials with certain on-demand properties.