A full-scale topology optimization method for surface fiber reinforced additive manufacturing parts

Abstract

Additive manufacturing provides new design space for fiber-reinforced composite structures given the flexibility in fiber layout and substrate material distribution. Hence, the present research develops a topology optimization method dedicated for surface fiber reinforcement design. The targeting part is composed of fiber contents reinforcing the boundary contour and isotropic substrate materials infilling the structural interior. The idea of full-scale modeling of the fiber contents and the approach of addressing the bi-modulus reinforcement effect are highlighted. Specifically, under the SIMP framework, the fiber contents (including the fiber materials and the wrapping matrix materials) are identified through boundary layer extraction with double layers of density smoothing and projection, and the reinforcement fiber materials are recognized through analytical skeleton extraction from the boundary layer. In this manner, constant-thickness fiber contents are modeled and more importantly, stringently incorporated into the topology optimization problem formulation. Both the boundary layer thickness and the fiber reinforcement thickness can be explicitly controlled by modifying the projection threshold parameters. Compliance minimization problems are considered in the current study. Sensitivities of the objective and constraint functions are derived to guide the design update. Several numerical and experimental examples are provided to demonstrate the validity and effectiveness of the proposed method, especially disclosing the impact on the derived topological structures by incorporating boundary layer fibers.