Investigation of reinforcement learning applied to gradient voronoi energy absorption structures design
Year: 2026
Type: article
Abstract: Voronoi structures can dissipate impact kinetic energy through plastic deformation. Their energy absorption capacity depends on design parameters like cell size and distribution. However, traditional trial-and-error design is inefficient. This study proposes an integrated Deep Reinforcement Learning (DRL) and Finite Element Method (FEM) framework to efficiently design high-performance Voronoi structures. Using Polylactic Acid (PLA), finite element simulations and collision experiments were conducted to validate the reliability of the numerical model under the investigated impact condition. A DRL+FEM framework was developed. A Double Deep Q-Network (DDQN) agent was trained to maximize energy absorption. Results showed that negatively graded Voronoi structures provide improved overall performance under the adopted optimization criterion. After optimization, the two-layer structure achieved a specific energy absorption and crushing force efficiency of 6.51. The three-layer structure saw improvements of 1.08 times and 1.71 times in these metrics, respectively, while the four-layer structure’s crushing force efficiency increased by 0.97 to 1.16 times compared to pre-optimization levels. The framework successfully reveals the influence of gradient characteristics.
Institutions: Shandong University, Beijing Institute of Technology
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
Awards: G2792199009, G601542656, G638372985
OpenAlex ID: W7128502049