Machine learning assisted prediction and analysis of in-plane elastic modulus of hybrid hierarchical square honeycombs

Abstract

In this study, experimental, finite element (FE) simulation, machine learning (ML), and theoretical techniques are employed to investigate the in-plane elastic modulus (EHHSH) of hybrid hierarchical square honeycombs (HHSHs). First, HHSHs with different configurations were fabricated using a 3D printer, and in-plane quasi-static compression tests were conducted on them. Then, 234 FE models are simulated to determine the EHHSH of HHSHs with various configurations, and the results are used to train 11 ML models. Comparative analysis demonstrates that the Extreme Gradient Boosting (XGBoost) model has the best predictive capability. Moreover, a modified theory for EHHSH is established based on the XGBoost model and existing theory, and its exceptional predictive capability is verified by comparing with experimental, FE, and existing theoretical results. Finally, the upper and lower bounds of EHHSH are determined by the modified theory, and the Shapley Additive Explanation (SHAP) method is used to identify the importance of different geometric parameters on tailoring EHHSH. The combination of theoretical and ML techniques provides a promising approach for developing a robust prediction model of material properties.