NON-PERIODIC LATTICE STRUCTURE DESIGN FOR ADDITIVE MANUFACTURING

Department of Mechanical EngineeringAs manufacturability of lattice structures has been relaxed with the availability of additive manufacturing (AM) technology, the study of cellular structure optimization has seen a rapid development during the past decade. Numerous design approaches for lattice structures have been proposed to help designers fabricate efficient lattice model. Generally, these approaches demand for unbearable computational cost and prior knowledge. To overcome the drawbacks of existing methods, Choi et al. proposes a simple framework of generating non-periodic lattice structures using topologically pre-optimized building blocks. However, this method does not properly consider the manufacturability of the lattice structure by neglecting additive manufacturing constraints in the design process. This thesis suggests a strategy to consider manufacturing constraints for the AM process in a contemporary lattice structure generation framework, in this case, Choi et al. work. The proposed method is devised to take full advantage of the already existing components, i.e. building block library, in order not to add complexity in the overall process. Considering the manufacturability of the lattice designs, an algorithm derived from the STL slicing method is introduced in the selection process to replace unprintable building blocks for optimal microstructure. Finally, numerical examples are presented, and reasonable solutions have been obtained to show the feasibility of the proposed method.clos

Direct computation of minimal rotation for support slimming

Abstract — To reduce the usage of supporting structure in additive manufacturing, an orientation-driven shape optimizer was developed in our prior work [Hu et al. 2015] which employs a volumetric mesh enclosing the input 3D models as the domain of computation. The orientation of a model is computed indirectly by surface of the volumetric mesh. In this paper, we extend our indirect computation to an approach that the computation is directly based on the information of input models by an algorithm using incremental linear programming and K-means clustering. The performance of this approach is decoupled from the shape similarity between the volumetric mesh and the input model. As a result, representations obtained from simpler volumetric decomposition such as voxels can be adopted as the domain of computation