Publications

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.

Mechanical metamaterials are regularly implemented in engineering applications due to their unique properties derived from their structural geometry and material composition. This study incorporates deep reinforcement learning, a subset of machine learning that teaches an agent to complete a task through interactive experiences, into mechanical metamaterial design. The approach creates a design environment for the reinforcement learning agent to iteratively construct metamaterials with tailorable deformation and hysteretic characteristics. Validation involved producing metamaterials with a thermoplastic polyurethane (TPU) base material that exhibited the deformation response of expanded thermoplastic polyurethane (E-TPU) while maximizing or minimizing hysteresis in cyclic compression. This alignment confirmed the feasibility of tailoring deformation and energy manipulation using mechanical metamaterials. The agent's generalizability was tested by tasking it to create various metamaterials with distinct loading deformation responses and specific hysteresis goals in a simulated setting. The agent consistently delivered metamaterials that met loading curve criteria and demonstrated favorable energy return. This work demonstrates the potential of deep reinforcement learning as a rapid and effective tool for designing mechanical metamaterials with customizable traits. It ushers in the possibility of on-demand metamaterial design solutions, opening avenues across industries like footwear, wearables, and medical equipment.

Interlocking metasurfaces (ILMs) are a new class of mechanical metasurfaces built from architected arrays of interlocking features that can serve as a nonpermanent, robust joining technology. An ILM's strength is governed by the structural material, orientation, and topology of its latching unit cells. The presented work optimized the topologies of ILM unit cells to maximize strength in tensile and shear loading using gradient-based parametric optimization and genetic algorithms. Experimental validation confirmed that the optimized designs achieved considerable strength increases compared to a human intuitive design. In several cases, the optimized designs were approximately double the effective interfacial strength compared to that achieved via expert intuition alone. The strength improvement was seen for isolated unit cells and arrays of interacting unit cells (metasurfaces). An analysis of the topologies of the optimized designs showed that tall dendritic geometric features with large contact surfaces yield robust solutions in tension, while short and broad geometric features with large contact surfaces yield better results in shear loading. This study revealed the importance of shape optimization to maximize ILM effectiveness under single- and multi-objective scenarios.

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