The Data-Driven Performance Prediction of Lattice Structures: The State-of-the-Art in Properties, Future Trends, and Challenges

Lattice structures, with their unique design, offer properties like a programmable elastic modulus, an adjustable Poisson’s ratio, high specific strength, and a large specific surface area, making them the key to achieving structural lightweighting, improving impact resistance, vibration suppression, and maintaining high thermal efficiency in the aerospace field. However, functional prediction and inverse design remain challenging due to cross-scale effects, extensive spatial freedom, and high computational costs. Recent advancements in AI have driven progress in predicting lattice structure functionality. This paper begins with an introduction to the lattice types, their properties, and applications. Then the development process for the performance-prediction methods of lattice structures is summarized. The current applications of performance-prediction methods, which are data-driven and related to material properties, structural properties, and performance under conditions of coupled multi-physical fields, are analyzed, and this analysis further extends to the data-driven methods in relation to their prediction of lattice structure functionality. This paper summarizes the application of data-driven methods in the prediction of the mechanical, energy absorption, acoustic, and thermal properties of lattice structures; elaborates on the application of these methods in the optimization design of lattice structures in the aerospace field; and details the relevant theory and references for the field of lattice structure performance analysis. Finally, the progress and problems in the functional prediction of lattice structures under the current research is demonstrated, and the future development direction of this field is envisioned.