Bayesian Optimization of Energy-Absorbing Lattice Structures for Additive Manufacturing

Abstract

Abstract Additive manufacturing has enabled the production of intricate lattice structures that meet stringent design requirements with minimal mass. While many methods such as lattice-based topology optimization are being developed to design lightweight structures for static loading, there is a need for design tools for achieving dynamic loading requirements. Lattice structures have shown particular promise as low-mass energy absorbers, but the computational expense of nonlinear finite element analysis and the difficulty of obtaining objective gradient information has made their optimization for impact loading particularly challenging. This study proposes a Bayesian optimization framework to determine the lattice structure design that provides the best performance under a specified impact, while managing the structure’s mass. Considering nonlinear effects such as plasticity and strain rate sensitivity, a 2D explicit finite element (FE) model is constructed for two lattice unit cell types under impact, and parameterized with respect to geometric attributes such as height, width, and strut thickness. These parameters are considered design variables in a minimization problem with an objective function that balances part volume with a common injury metric, the head injury criterion (HIC). Penalty values are assigned to designs that fail to absorb the entire impact. Design for additive manufacturing (DFAM) constraints including minimum feature thickness and maximum overhang angle are applied to ensure that the optimal design can be manufactured without subsequent manual refinement or post-processing. The best optimizer hyperparameters are then carried over into larger optimization problems involving lattice structures. Future work could include expanding this framework to allow for lattice structure designs with arbitrary boundaries.