Stress-driven generative design and numerical assessment of customized additive manufactured lattice structures
Fuyuan Liu, Min Chen, Sanli Liu, Zhouyi Xiang, Songhua Huang, Eng Gee Lim, Shunqi Zhang
Abstract
The rise of additive manufacturing (AM) has positioned lattice infilling as a pivotal strategy for creating lightweight, customized engineering components. This study presents a generative method that enables the conformal design and stiffness prediction of complex gradient strut-node lattice structures. A stress-driven Multi-Agent System (MAS) is introduced for the parametric optimization of lattice material distribution, incorporating geometric limitations, stress factors, and AM constraints. A beam element model simplifies the numerical analysis of the structures' linear stiffness. By applying the Response Surface Method (RSM), we develop a numerical model that evaluates the sensitivity of MAS design variables and predicts mechanical performance. By applying the Response Surface Method (RSM), a numerical model is established by the Response Surface Method (RSM), not only conducting a quantitative analysis on the sensitivity of MAS's design variables but predicting mechanical performance. This method is validated by designing a supporting component, demonstrating that the optimized lattice design can achieve a linear stiffness 1.4 times greater than that of conventional uniform lattice infills for the same mass. This research provides a comprehensive framework for the efficient design and analysis of irregular lattice structures at a macroscopic scale.