Reinforcement Learning-Driven Optimization of Additively Manufactured Lattices for Enhancement of Mechanical Stiffness and Lightweight Property

Additive manufacturing (AM) has been extensively used in various industries to produce complex-shaped parts, enabling the application of advanced design methodologies for lightweight and mechanically optimized structures. Reinforcement learning (RL) has emerged as a powerful tool for optimizing complex structural designs in various mechanical systems. In this study, an RL-based strategy is proposed to optimize the design of three-dimensional lattice structures. RL is employed to optimize the shape variables of each unit cell in a lattice with the aim of maximizing the mechanical stiffness while retaining a lightweight design. The research object is a body-centered cubic (BCC) lattice with strut geometries adjusted through RL optimization to enhance structural performance. The RL environment, which incorporates the state, reward, and action, is integrated into a finite element method simulation, in which the actions derive a set of optimal design variables through the learning process. The feasibility and effectiveness of the RL-generated designs are validated by fabricating optimized structures using a vat photopolymerization AM process and experimentally testing them under three-point bending. The results demonstrate that the RL-optimized lattice structures exhibit superior performance compared to traditional BCC lattice designs. This study highlights the potential of RL for design optimization and its broad applicability in various engineering systems that require complex mechanical components.