  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.
