Data-driven design for additive manufacturing of energy absorption lattice structures with variable density

Three-dimensional (3D) lattice structures have garnered significant attention in aerospace, architecture, automotive, and medical applications due to their lightweight and superior energy absorption capabilities. Additive manufacturing (AM) enables the fabrication of complex lattice geometries with customized mechanical properties, making them ideal structures for energy absorption scenarios. However, optimizing these structures to achieve spatially varying density distributions for enhanced performance remains a significant challenge. In this study, a data-driven design framework has been proposed for the AM of energy absorption lattice structures with spatially graded densities. The approach enables the tailoring of geometric parameters, including cell arrangement and strut diameters, to realize variable-density architectures optimized for specific performance requirements. The proposed framework is validated through experimental testing of 3D-printed lattice specimens. Compared to the lattices with uniformly distributed cells, the variable-density structures evidence a 218 % increase in maximum load and a 246 % improvement in specific energy absorption. The finite element analysis and experimental comparisons are used to investigate the influence of relative density gradients on energy absorption performance, peak stress mitigation, and deformation. The results highlight the effectiveness of the data-driven design approach in enabling the fabrication of functionally graded lattice structures with enhanced mechanical performance.