Data-Driven Parametric Design of Additively Manufactured Hybrid Lattice Structure for Stiffness and Wide-Band Damping Performance

The outstanding comprehensive mechanical properties of newly developed hybrid lattice structures make them useful in engineering applications for bearing multiple mechanical loads. Additive-manufacturing technologies make it possible to fabricate these highly spatially programmable structures and greatly enhance the freedom in their design. However, traditional analytical methods do not sufficiently reflect the actual vibration-damping mechanism of lattice structures and are limited by their high computational cost. In this study, a hybrid lattice structure consisting of various cells was designed based on quasi-static and vibration experiments. Subsequently, a novel parametric design method based on a data-driven approach was developed for hybrid lattices with engineered properties. The response surface method was adopted to define the sensitive optimization target. A prediction model for the lattice geometric parameters and vibration properties was established using a backpropagation neural network. Then, it was integrated into the genetic algorithm to create the optimal hybrid lattice with varying geometric features and the required wide-band vibration-damping characteristics. Validation experiments were conducted, demonstrating that the optimized hybrid lattice can achieve the target properties. In addition, the data-driven parametric design method can reduce computation time and be widely applied to complex structural designs when analytical and empirical solutions are unavailable.