Inverse design framework of hybrid honeycomb structure with high impact resistance based on active learning

In this study, an inverse design framework was established to find lightweight honeycomb structures (HCSs) with high impact resistance. The hybrid HCS, composed of re-entrant (RE) and elliptical annular re-entrant (EARE) honeycomb cells, was created by constructing arrangement matrices to achieve structural lightweight. The machine learning (ML) framework consisted of a neural network (NN) forward regression model for predicting impact resistance and a multi-objective optimization algorithm for generating high-performance designs. The surrogate of the local design space was initially realized by establishing the NN in the small sample dataset, and the active learning strategy was used to continuously extended the local optimal design until the model converged in the global space. The results indicated that the active learning strategy significantly improved the inference capability of the NN model in unknown design domains. By guiding the iteration direction of the optimization algorithm, lightweight designs with high impact resistance were identified. The energy absorption capacity of the optimal design reached 94.98% of the EARE honeycomb, while the initial peak stress and mass decreased by 28.85% and 19.91%, respectively. Furthermore, Shapley Additive Explanations (SHAP) for global explanation of the NN indicated a strong correlation between the arrangement mode of HCS and its impact resistance. By reducing the stiffness of the cells at the top boundary of the structure, the initial impact damage sustained by the structure can be significantly improved. Overall, this study proposed a general lightweight design method for array structures under impact loads, which is beneficial for the widespread application of honeycomb-based protective structures. • A hybrid honeycomb structure is designed to reduce the mass and manufacturing cost. • A multi-objective optimization design for hybrid honeycomb structures is conducted. • Active learning significantly reduces the number of samples required for NN training. • Shapley Additive Explanations are used to explain the NN model.