Additive manufacturing-driven design optimization: Building direction and structural topology

Abstract Additive manufacturing (AM) has been adopted by high-value-added industries to revolutionize product life cycle performance, from advantageous design topology to functional advances. Compared with conventional manufacturing methods such as tooling and molding, the design constraints induced by AM usually include support structures, building direction (BD), feedstock material properties, and process parameters. These new factors need to be considered in AM-driven product design and manufacturing. In this study, an AM-driven topology optimization method coupled with a transversely isotropic material model and solid anisotropic material with penalization (SAMP) is proposed to establish a quantitative correlation between process-related parameters and the mechanical properties of printed materials, further implementing such correlation for process and topology optimization using a gradient-based algorithm. Specifically, the coordinate transformation matrix is combined with the transversely isotropic stiffness and strength of the printed material using stereolithography apparatus (SLA) to describe the elastic matrix under different BDs. Thereafter, case-dependent product performances are investigated based on an integrated method considering the net-effect of structural design and BD. The proposed approach can easily achieve AM-driven topology optimization of complex products with desirable mechanical performance. Furthermore, the established topological model can be broadly applied to complex functional part design and optimization, as well as case studies on AM-driven product evaluation.