Simulation driven lattice structure parameters optimization using genetic algorithm based technique for UAV wing

Reducing structural mass is a primary route for lowering fuel consumption and emissions in aerospace structures. Additive manufacturing (AM) enables the fabrication of complex lattice architectures, creating new opportunities for lightweight structural design. However, selecting suitable lattice parameters, including unit-cell topology, cell size, and strut thickness, remains challenging due to the large design space and complex parameter interactions. This study presents a simulation-driven, field-based lattice optimization framework to establish the relationships between lattice topology, cell size, strut thickness, and structural performance in a lattice-infilled unmanned aerial vehicle (UAV) wing using nTop, ANSYS Workbench, and a genetic-algorithm-based optimization approach. The proposed methodology employs a two-stage optimization strategy. In the first stage, different lattice topologies are evaluated to establish the relationships between lattice parameters and structural performance. The results show that cell size is the dominant design parameter governing both structural mass and stiffness, while lattice topology influences load-transfer behavior and stress distribution. Among the investigated configurations for the present wing geometry and loading conditions, the Octet lattice (22×22×22 mm) exhibited the most favorable overall performance, achieving a 30.68% mass reduction relative to the baseline wing while maintaining a maximum displacement of 0.92 mm and a peak von Mises stress of approximately 79 MPa. The Octet topology was followed by the BCC, Kelvin, Fluorite, and FCC lattices in terms of overall structural efficiency. In the second stage, the optimum topology is extended through a stress-field-driven grading approach, where finite-element-derived stress fields are mapped to local cell-size (5–30 mm) and strut-thickness (0.5–2 mm) distributions. This adaptive strategy redistributes material according to local structural demand, assigning smaller cells and thicker struts to highly stressed regions while allocating larger cells and thinner struts to low-stress regions. The resulting functionally graded lattice structures achieved an additional 5–15% mass reduction relative to the corresponding uniform lattice designs, with the best-performing configuration achieving a total mass reduction of 43.79% relative to the conventional spar-rib wing. Furthermore, peak stresses were reduced by 12–20% compared with uniform lattice designs and by 30–35% compared with the conventional spar-rib wing while satisfying the prescribed displacement constraint. The results demonstrate that the proposed framework enables efficient automated exploration of the lattice design space and provides a systematic methodology for the design of lightweight, functionally graded lattice-infilled aerospace structures.