Topology optimization of fiber-reinforced structures with discrete fiber orientations for additive manufacturing

Abstract

Additive manufacturing (AM) has revolutionized the way we design and manufacture lightweight composite structures with complex geometries and extraordinary performance. In composite AM, fibers are often steered within the plane of printing and sometimes at predefined discrete angles. Hence, designing structures for AM must consider such manufacturing constraints along with the concurrent optimization of structures and fiber orientations. Herein, we propose a method that uses a penalized normal distribution (PND) function to design the fiber orientation based on predefined discrete angles. By discretizing a continuous design variable and penalizing the effective properties, the method effectively drives the design variable to converge to one of the target candidates with low deviations. Using only one design variable at each spot, the method is scalable and can be easily adapted as the number of candidates changes. By coupling the discrete angle optimization with structural optimization, the multiscale method concurrently optimizes the structural topology with fiber orientations considering AM constraints. Numerical examples demonstrate the advantages of this framework and its extension to solving 3D problems.