Robust topology optimization of structures considering additive manufacturing-induced material anisotropy and uncertainty
Hexin Jiang, Zhicheng He, Hailun Tan, Eric Li
Abstract
The integration of topology optimization and additive manufacturing (AM) is seen as a pivotal approach for creating products with high added value. Nevertheless, the inherent layer-by-layer fabrication process of AM and the widespread manufacturing errors lead to both anisotropy and uncertainty in the printed parts, which poses challenges to constructing material models and optimization strategies. To address these issues, this paper presents a robust topology optimization (RTO) approach coupled with an anisotropic material model and a hybrid interval random model for additively manufactured structures. The method utilizes the bi-directional evolutionary structural optimization (BESO) framework and defines the uncertain material parameters with anisotropic mechanical behavior. An efficient hybrid uncertainty perturbation analysis (HUPA) method is then proposed for estimating the robust objective function, and the sensitivity values of the design variables are further derived. Several 2D and 3D numerical examples are given to verify the effectiveness of the proposed method. The results show that both the material off-angle and the material properties fluctuation exert significant influences on the structural performance, indicating the necessity of considering both anisotropy and uncertainty caused by the AM process in engineering structural optimization.