Deep reinforcement learning for the rapid on-demand design of mechanical metamaterials with targeted nonlinear deformation responses

Abstract

Mechanical metamaterials are artificial materials with unique global properties due to the structural geometry and material composition of their unit cell. Typically, mechanical metamaterial unit cells are designed such that, when tessellated, they exhibit unique mechanical properties such as zero or negative Poisson's ratio and negative stiffness. Beyond these applications, mechanical metamaterials can be used to achieve tailorable nonlinear deformation responses. Computational methods such as gradient-based topology optimization (TO) and size/shape optimization (SSO) can be implemented to design these metamaterials. However, both methods can lead to suboptimal solutions or a lack of generalizability. Therefore, this research used deep reinforcement learning (DRL), a subset of deep machine learning that teaches an agent to complete tasks through interactive experiences, to design mechanical metamaterials with specific nonlinear deformation responses in compression or tension. The agent learned to design the unit cells by sequentially adding material to a discrete design domain and being rewarded for achieving the desired deformation response. After training, the agent successfully designed unit cells to exhibit desired deformation responses not experienced during training. This work shows the potential of DRL as a high-level design tool for a wide array of engineering applications.