Memory-Efficient Modeling and Slicing of Large-Scale Adaptive Lattice Structures

Lattice structures have been widely used in various applications of additive manufacturing due to its superior physical properties. If modeled by triangular meshes, a lattice structure with huge number of struts would consume massive memory. This hinders the use of lattice structures in large-scale applications (e.g., to design the interior structure of a solid with spatially graded material properties). To solve this issue, we propose a memory-efficient method for the modeling and slicing of adaptive lattice structures. A lattice structure is represented by a weighted graph where the edge weights store the struts' radii. When slicing the structure, its solid model is locally evaluated through convolution surfaces and in a streaming manner. As such, only limited memory is needed to generate the toolpaths of fabrication. Also, the use of convolution surfaces leads to natural blending at intersections of struts, which can avoid the stress concentration at these regions. We also present a computational framework for optimizing supporting structures and adapting lattice structures with prescribed density distributions. The presented methods have been validated by a series of case studies with large number (up to 100M) of struts to demonstrate its applicability to large-scale lattice structures.

Conceptual Design by a Topology Consistent Skeletal Modeler With Convolution Surfaces

During the conceptual design stage, the design engineers usually sketch their design ideas. For those sketches, the skeleton of the design idea can be created with lower dimensional primitives like lines, arcs, etc. In this paper, we focus on skeletal modeling, which is an approach to creating solid models in which the engineer designs with lower dimensional primitives such as points, lines, and triangles. The skeleton is then “skinned over” to create the surfaces of the three dimensional object. Then the convolution surfaces are generated by convolving a kernel function with a geometric field function to create an implicit surface. We propose that skeleton, even it is simple, contains important design information, such as the geometric, topology that defines the design concept. It is very important to keep the topology of the skeleton and thus the important information that defines the design concept, i.e, the geometry of the product, the functionality of the product determined by the topology of the design. We assume that design engineers expect the topology of a skeletal model to be identical to that of the underlying skeleton. In this paper, the system is described and some examples are illustrated to use the skeletal based modeler.